Intra-flow

Loss

Recovery

and

Control

for

VolP

Henning

Sanneck

GMD

Fokus

Nguyen

Tuong

Long

Le

GMD

Fokus

Kaiserin-Augusta-Allee

Kaiserin-Augusta-Allee

D-l

Berlin,

Germany

D-l

Berlin,

Germany

sanneck@fokus.gmd.de

le@fokus.gmd.de

Adam

Wolisz

Technical

University

Berlin,

Telecommunication

Networks

Group,

Sekr.

FT

Einsteinufer

D-l

Berlin,

Germany

wolisz@ee.tu-berlin.de

ABSTRACT

Best

effort

packet-switched

networks,

like

the

Internet,

do

not

offer

a

reliable

transmission

of

packets

to

applications

with

real-time

constraints

such

as

voice.

Thus,

the

loss

of

packets

impairs

the

application-level

utility.

For

voice

this

utility

impairment

is

twofold:

on

one

hand,

even

short

bursts

of

lost

packets

may

decrease

significantly

the

ability

of

the

receiver

to

conceal

the

packet

loss

and

the

speech

signal

playout

is

interrupted.

On

the

other

hand,

some

packets

may

be

particular

sensitive

to

loss

as

they

carry

more

important

information

in

terms

of

user

perception

than

other

packets.

We

first

develop

an

end-to-end

model

based

on

loss

runlengths

with

which

we

can

describe

the

loss

distribution

within

a

flow.

These

packet-level

metrics

are

then

linked

to

user-level

objective

speech

quality

metrics.

Using

this

framework,

we

find

that

for

low-compressing

sample-based

codecs

(PCM)

with

loss

concealment

isolated

packet

losses

can

be

concealed

well,

whereas

burst

losses

have

a

higher

perceptual

impact.

For

high-compressing

frame-based

codecs

on

one

hand

the

impact

of

loss

is

amplified

through

error

propagation

caused

by

the

decoder

filter

memories,

though

on

the

other

hand

such

coding

schemes

help

to

perform

loss

concealment

by

extrapolation

of

decoder

state.

Contrary

to

sample-based

codecs

we

show

that

the

concealment

performance

may

break

at

transitions

within

the

speech

signal

however.

We

then

propose

mechanisms

which

differentiate

between

packets

within

a

voice

data

flow

to

minimize

the

impact

of

packet

loss.

We

designate

these

methods

as

intra-flow

loss

recovery

and

control.

At

the

end-to-end

level,

identification

of

packets

sensitive

to

loss

(sender)

as

well

as

loss

concealment

(receiver)

takes

place.

Hop-by-hop

support

schemes

then

allow

to

(statistically)

trade

the

loss

of

one

packet,

which

is

considered

more

important,

against

another

one

of

the

same

flow

which

is

of

lower

importance.

As

both

packets

require

the

same

cost

in

terms

of

network

transmission,

a

gain

in

user

perception

is

obtainable.

We

show

that

significant

speech

quality

improvements

can

be

achieved

and

additional

data

and

delay

overhead

can

be

avoided

while

still

maintaining

a

network

service

which

is

virtually

identical

to

best

effort

in

the

long

term.

Categories

and

Subject

Descriptors

[Computer

Communication

Networks]:

Distributed

Systems-Distributed

applications;

[Computer

Communication

Networks]:

Internetworking-Routers;

[Computer

Systems

Organization]:

Performance

of

techniques;

[Computer

Systems

Organization]:

Performance

of

Systems--Modeling

techniques

General

Terms

Design,

Measurement,

Performance,

Reliability

INTRODUCTION

Considering

that

a

real-time

flow

may

experience

some

packet

loss,

the

impact

of

loss

may

vary

significantly

dependent

on

which

packets

are

lost

within

a

flow.

In

the

following

we

distinguish

two

reasons

for

such

a

variable

loss

sensitivity:

Temporal

sensitivity:

Loss

of

ADUs"

which

is

correlated

in

time

may

lead

to

disruptions

in

the

service.

Note

that

this

effect

is

further

aggravated

by

some

interdependence

between

ADUs

(i.e.,

that

one

ADU

can

only

be

decoded

when

a

previous

ADU

before

has

successfully

been

received

and

decoded).

For

voice,

as

a

single

packet

contains

typically

several

ADUs

(voice

frames)

this

effect

is

thus

more

significant

than

e.g.

for

video.

It

translates

basically

to

isolated

packet

losses

versus

losses

that

occur

in

bursts.

Sensitivity

due

to

ADU

heterogeneity:

Certain

ADUs

might

contain

parts

of

the

encoded

signal

which

are

‘Application

Data

Unit:

the

unit

of

data

emitted

by

a

source

coder

such

as

a

video

or

voice

frame.

Figure

Schematic

utility

functions

dependent

on

the

loss

of

more

(+l)

and

less

important

packets

more

important

with

regard

to

user

perception

than

others

of

the

same

flow.

Let

us

consider

a

flow

with

two

frame

types

of

largely

different

perceptual

importance

(we

assume

same

size,

frequency

and

no

interdependence

between

the

frames).

Under

the

loss

of

of

the

packets,

the

perceptual

quality

varies

hugely

between

the

case

where

the

of

the

frames

with

high

perceptual

importance

are

received

and

the

tax

where

the

less

important

frames

are

received.

Network

support

for

real-time

multimedia

flows

can

on

one

hand

aim

at

offering

a

lossless

service,

which,

however,

to

be

implemented

within

a

pa&et-switched

network,

will

be

costly

for

the

network

provider

and

thus

for

the

user.

On

the

other

hand,

within

a

lossy

service,

the

above

sensitivity

constraints

must

be

taken

into

account.

It

is

our

strong

belief

that

this

needs

to

be

done

in

a

generic

way,

i.e.,

no

application-specific

knowledge

(about

particular

coding

schemes

e.g.)

should

be

necessary

within

the

network

and,

vice

versa,

no

knowledge

about

network

specifics

should

be

necessary

within

an

application.

Let

us

now

consider

the

case

that

of

packets

of

a

flow

are

identified

as

more

important

(designated

by

+l)

or

less

important

due

to

any

of

the

above

sensitivity

constraints.

Figure

a)

shows

a

generic

utility

function

describing

the

applicationlevel

Quality

of

Service

(QoS)

dependent

on

the

percentage

of

packets

lost.

For

real-time

multimedia

traffic,

such

utility

should

correspond

to

perceived

video/voice

quality.

If

the

relative

importance

of

the

packets

is

not

known

by

the

transmission

system,

the

loss

rates

for

the

+l

and

packets

are

equal.

Due

to

the

over-proportional

sensitivity

of

the

+l

packets

to

loss

as

well

as

the

dependence

of

the

end-t*

end

loss

recovery

performance

on

the

fl

packets,

the

utility

function

is

decreasing

significantly

in

a

non-linear

way

(approximated

in

the

figure

by

piece-wise

linear

functions)

with

an

increasing

loss

rate.

Figure

b)

presents

the

CBS~

where

all

+l

packets

are

protected

at

the

expense

of

pa&&.

The

decay

of

the

utility

function

(for

loss

rates

is

reduced,

because

the

+l

packets

are

protected

and

the

endto-end

loss

recovery

can

thus

operate

properly

over

a

wider

range

of

loss

rates

indicated

by

the

shaded

area.

This

results

in

a

graceful

degradation

of

the

application"s

utility.

Note

that

the

higher

the

non-linearity

of

the

utility

contribution

of

the

fl

packets

is

(deviation

from

the

dotted

curve

in

Fig.

a),

the

higher

is

the

potential

gain

in

utility

when

the

protection

for

+l

packets

is

enabled.

Results

for

actual

perceived

quality

as

utility

for

multimedia

applications

exhibit

such

a

non-linear

behavior*.

To

describe

this

effect

and

provide

a

taxonomy

for

different

QoS

enhancement

approaches,

we

introduce

a

novel

terminology:

we

designate

mechanisms

which

influence

QoS

parameters

between

Bows

(thus

decrease

the

loss

rate

of

one

flow

at

the

expense

of

other

flows)

as

inter-flow

QoS.

Schemes

which,

in

the

presence

of

loss,

differentiate

between

pa&&

within

a

flow

as

demonstrated

in

Figure

above,

provide

intra-flow

QoS

enhancement.

As

QoS

mechanisms

have

to

be

implemented

within

the

network

(hopby-hop)

and/or

in

the

end

systems

(end-to-end),

we

have

another

axis

of

classification.

The

adaptation

of

the

sender"s

bitrate

to

the

current

network

congestion

state

as

an

intraflow

QoS

scheme

(loss

avoidance,

[IS])

is

difficult

to

apply

to

voice.

Considering

that

voice

flows

have

a

very

low

bitrate,

the

relative

cost

of

transmitting

the

feedback

information

is

high

(when

compared

e.g.

to

a

video

flow).

To

reduce

this

cost

the

feedback

interval

would

need

to

be

increased,

then

leading

to

a

higher

probability

of

wrong

adaptation

decisions.

The

major

however,

is

the

lack

of

a

codec

which

is

truly

scalable

in

terms

of

its

output

bitrate

and

corresponding

perceptual

quality.

Currently

standardized

voice

codecs

usually

only

have

a

output

bitrate.

While

it

has

been

proposed

to

switch

between

voice

codeca

the

MOS

(subjective

quality)

values

for

the

codecs

employed

do

not

differ

much:

e.g.,

theITU

codecs

cover

a

bitrate

range

from

kbitjs

to

kbitjs

while

the

subjective

quality

differs

by

less

than

on

a

MOS

scale

bad,

excellent

quality).

So

when

the

availability

of

sutticient

computing

power

is

assumed,

the

lowest

bitrate

codec

can

be

chosen

permanently

without

actually

decreasing

the

perceptual

quality.

For

loss

recovery

on

an

end-to-end

basis,

due

to

the

realtime

delay

constraints,

open-loop

schemes

like

Forward

Error

Correction

(FEC)

have

been

proposed

While

such

schemes

are

attractive

because

they

can

be

used

on

the

Internet

today,

they

also

have

several

drawbacks.

The

amount

of

redundant

information

needs

to

be

adaptive

to

avoid

taking

bandwidth

away

from

other

flows.

This

adaptation

is

crucial

especially

when

the

fraction

of

traflic

using

redundancy

schemes

is

large

If

the

redundancy

is

a

source

coding

itself,

like

it

has

often

been

proposed

the

comments

from

above

on

adaptation

also

apply.

Using

redundancy

has

also

implications

to

the

playout

delay

adaptation

([lo])

employed

to

de-jitter

the

packets

at

the

receiver.

Note

that

the

presented

types

of

loss

sensitivity

also

apply

to

ap‘While

we

have

obtained

results

which

confirm

the

shape

of

the

overall

utility

curve

shown

in

Fig.

clearly

the

utility

functions

of

the

sub.flows

and

their

relationship

are

more

complex

and

only

approximately

additive.

Table

State

and

transition

probabilities

computed

for

an

end-to-end

Internet

trace

using

a

general

Markov

model

(third

order)

by

Yajnik

et.

al.

state

Probability

of

Probability

Probability

being

in

the

state

of

of

plications

which

are

enhanced

by

end-to-end

loss

recovery

mechanisms.

End-to-end

mechanisms

can

reduce

and

shift

such

sensitivities

but

cannot

come

close

to

eliminate

them.

Therefore

in

this

work

we

assume

that

the

lowest

possible

bitrate

which

provides

the

desired

quality

is

chosen.

Neither

feedback/adaptation

nor

redundancy

is

used,

however,

at

the

end-to-end

level,

identification/marking

of

packets

sensitive

to

loss

(sender)

as

well

as

loss

concealment

(receiver)

takes

place.

Hop-by-hop

support

schemes

then

allow

trading

the

loss

of

one

packet,

which

is

considered

more

important,

against

another

one

of

the

same

flow

which

is

of

lower

importance.

We

employ

actual

codecs

and

measure

their

utility

in

the

presence

of

packet

loss

using

objective

speech

quality

measurement.

The

paper

is

structured

as

follows:

Section

introduces

packet-

and

user-level

metrics.

We

employ

these

metrics

to

describe

the

sensitivity

of

VoIP

traffic

to

packet

loss

in

section

Section

briefly

introduces

a

queue

management

algorithm

which

can

be

used

for

intra-flow

loss

control.

In

section

we

present

results

documenting

the

performance

of

the

proposed

mechanisms

at

both

the

end-to-end

and

hopby-hop

level.

Section

concludes

the

paper.

METRICS

Packet-level

metrics

A

general

Markov

model

S])

which

describes

the

loss

process

is

defined

as

follows:

Let

P(

Z(s)

Z(s

m),...

,Z(s

be

the

state

transition

probability

of

a

general

Markov

model

of

order

m,

where

Z(s)

is

the

loss

indicator

function

for

the

packet

with

the

sequence

number

s.

All

combinations

for

the

values

and

of

the

sequence

Z(s-m),

appear

in

the

state

space.

As

an

example

P(

Z(s)

Z(s

gives

the

state

transition

probability

when

the

current

packet

s

is

lost,

the

previous

packet

s

has

also

been

lost

and

packet

s

has

not

been

lost.

The

number

of

states

of

the

model

is

Two

state

transitions

can

take

place

from

any

of

the

states.

Thus,

the

number

of

parameters

which

have

to

be

computed

is

Even

for

relatively

small

m

this

amount

of

parameters

is

difficult

to

be

evaluated

and

compared.

Table

shows

some

values

for

the

state

and

transition

probabilities

for

a

general

Markov

model

of

third

order

measured

end-to-end

in

the

Internet

by

Yajnik

et.

al.

It

is

interesting

to

note

that

for

all

states

with

Z(s

the

probability

for

the

next

packet

not

to

be

lost

(Z(s)

is

generally

very

high

in

bold

typeface)

whereas

when

Z(s-

the

state

transition

probabilities

to

that

event

cover

the

range

of

to

That

means

that

past

no

loss

events

do

not

affect

the

loss

process

as

much

as

past

loss

events.

Intuitively

this

seems

to

make

sense,

because

a

successfully

arriving

packet

can

be

seen

as

a

indicator

for

congestion

relief.

Andren

et.

al.

([l])

as

well

as

Yajnik

et.

al.

both

confirmed

this

by

measuring

the

cross

correlation

of

the

loss-

and

no-loss-runlengths.

They

came

to

the

result

that

such

correlation

is

very

weak.

This

implies

that

patterns

of

short

loss

bursts

interspersed

with

short

periods

of

successful

packet

arrivals

occur

rarely

(note

in

this

context

that

in

‘&able

the

pattern

has

by

far

the

lowest

state

probability).

Thus,

in

the

following

we

employ

a

model

which

only

considers

the

past

loss

events

for

the

state

transition

probability.

The

number

of

states

of

the

model

can

be

reduced

from

to

m

This

means

that

we

only

consider

the

state

transition

probability

P(

Z(s)

Z(s

k),

,Z(s

with

Z(s

Ic

i)

V

i

E

Ic

where

Ic

k

m)

is

a

variable

parameter.

We

define

a

loss

run

length

lc

for

a

sequence

of

Ic

consecutively

lost

packets

detected

at

sj

(sj

Ic

with

Z(sj

k

O,Z(sj)

and

Z(sj

k

i)

V

i

E

Ic

j

being

the

j-th

‘<burst

loss

event.

Note

that

the

parameters

of

the

model

become

independent

of

the

sequence

number

s

and

can

now

rather

be

described

by

the

occurence

ok

of

a

loss

run

length

k.

We

define

the

random

variable

X

as

follows:

X

no

packet

lost,

X

k

lc

m):

ezactly

lc

consecutive

packets

lost,

and

X

k

k

m):

at

least

k

consecutive

packets

lost.

With

this

definition,

we

establish

a

loss

run-length

model

with

a

finite

number

of

states

(m

which

gives

loss

probabilities

dependent

on

the

burst

length.

In

the

model,

for

every

additional

lost

packet

which

adds

to

the

length

of

a

loss

burst

a

state

transition

takes

place.

If

a

packet

is

successfully

received,

the

state

returns

to

X

Thus,

the

state

probability

of

the

system

for

Ic

m

is

P(X

Zc).

Due

to

the

limited

memory

of

the

system,

the

last

state

X

m

is

just

defined

as

crm

consecutive

packets

lost,

with

P(X

m)

being

the

state

probability.

Given

the

case

of

a

finite

number

of

packets

a

for

a

flow,

which

experiences

d

]cok

packet

drops,

we

have

the

relative

frequency

pL,k

(P(X

k)

for

a

for

the

occurence

of

a

loss

burst

of

length

lc.

An

approximation

for

the

expectation

of

the

random

variable

X

can

be

computed

as

pi

cr==,

#+L,k

and

identified

with

the

mean

loss

rate.

We

can

also

approximate

the

state

probabilities

of

the

model

by

the

cumulative

loss

rate

p~,~,,~(k)

CrSkp~,,,

k

m)

and

p~,~

Cr.p=,

(n--mzl)ot

(k

m).

The

transition

probabilities

for

lc

m

can

be

computed

easily

as:

p(k--l)(k)

P(X>klX>k-l)=

ZZ

P

X

k

These

conditional

loss

probabilities

again

can

be

approxPzl,cum(k)

Cnm,k

on

i

m

a

t

e

d

by

PL,cond@)

PL,cum(k-l)

C;Ek--l

For

Figure

Loss

run-length

model

with

m

states

P(X

m]X

m)

we

have:

PL,,,d(rn)

%Lf;:P

Fig.

shows

the

Markov

chain

for

the

model.

Additionally,

we

also

define

a

random

variable

Y

which

de

scribes

the

distribution

of

burst

loss

lengths

with

respect

to

the

bunt

loss

events

j

(and

not

to

packet

events

like

in

the

definition

of

X).

We

have

the

burst

loss

length

rate

ga

as

an

estimate

for

P(Y

k).

Thus,

the

mean

burst

loss

length

is

g

h

e

CT==,

kgb

corresponding

to

E[Y]

(average

loss

gap,

The

run-length

model

implies

a

geometric

distribution

for

residing

in

state

X

m.

For

the

probability

of

a

burst

loss

length

of

k

packets

we

thus

can

compute

estimates

for

performance

parameters

in

a

higher

order

model

representation

(note

that

here

Y

represent

the

random

variable

used

in

the

higher-order

models).

For

a

three

state

model

we

have

e.g.

for

P(Y

k):

P(Y

k)

P(Y=k)=l-pm:

k=l

pn

p;;"

-pm):

k

For

the

special

case

of

a

system

with

a

memory

of

only

the

previous

packet

(m

l),

we

can

use

the

runlength

distribution

for

a

simple

computation

of

the

parameters

of

the

commonly-used

Gilbert

model

(Fig.

to

characterize

the

loss

process

(X

being

the

associated

random

variable

with

X

no

packet

lost,

X

a

packet

lost).

Then

the

probability

of

being

in

state

m

can

be

seen

BS

the

uncow

ditional

loss

probability

ulp

and

approximated

by

the

mean

loss

(pr.

pi,,,,).

Only

one

conditional

loss

probability

clp

for

the

transition

--t

exists:

p~,E-d

CFL(~

lbd

If

losses

of

one

flow

are

correlated

(i.e.,

the

loss

probability

of

an

arriving

packet

is

itiuenced

by

the

contribution

to

the

state

of

the

queue

by

a

previous

packet

of

the

same

flow

and/or

both

the

previous

and

the

current

packet

see

busty

arrivals

of

other

traffic,

we

have

pal

clp

and

thus

ulp

clp.

For

pal

elp

the

Gilbert

model

is

equivalent

to

a

l-state

(Bernotilli)

model

with

zllp

elp

(no

loss

correlation).

As

in

equation

we

can

compute

an

estimate

for

the

probability

of

a

burst

loss

length

of

k

packets

for

a

higher

order

model

representation:

B(Y

k)

clp)

Figure

Loss

run-length

(Gilbert

model)

model

with

two

states

Figure

Components

of

the

end-to-end

loss

recovery/control

measurement

setup.

Objective

Speech

Quality

Metrics

Unlike

the

conventional

methods

like

the

Signal-t-Noise

Ratio

(SNR),

novel

objective

quality

measures

attempt

to

estimate

the

subjective

quality

as

closely

as

possible

by

modeling

the

human

auditory

system.

In

our

evaluation

we

use

two

objective

quality

measures:

the

Enhanced

Mod&d

Bark

Spectral

Distortion

(EMBSD,

and

the

Measw

ing

Normalizing

Blocks

(MNB)

described

in

the

Appendix

II

of

the

ITU-T

Recommendation

These

two

objective

quality

measure.~

are

reported

to

have

a

very

high

correlation

with

subjective

tests,

their

relation

to

the

range

of

subjective

test

result

values

(MOS)

is

close

to

being

linear

and

they

are

recommended

as

being

suitable

for

the

evaluation

of

speech

degraded

by

transmission

errors

in

real

network

environments

such

as

bit

errors

and

frame

erasures

Both

metrics

are

distance

measures,

i.e.,

a

IB

suit

value

of

implies

perfect

quality,

the

larger

the

value,

the

worse

the

speech

quality

is

(in

Fig.

we

show

an

axis

with

approximate

corresponding

MOS

values).

For

all

simulations

in

this

paper

we

employed

both

schemes.

As

they

yielded

very

similar

results

(though

MNB

results

generally

exhibited

less

variability)

we

only

present

EMBSD

results.

VOIP

LOSS

SENSITIVITY

Figure

shows

the

components

of

the

measurement

setup

which

we

will

use

to

evaluate

our

approach

to

combined

end-t-end

and

hopby-hop

loss

recovery

and

control.

The

shaded

boxes

show

the

components

in

the

data

path

where

mechanisms

of

loss

recovery

are

located.

Together

with

the

parameters

of

the

network

model

(section

and

the

perceptual

model

we

obtain

a

measurement

setup

which

allows

us

to

map

a

specific

PCM

signal

input

to

a

speech

quality

measure.

While

using

a

simple

end-to-end

loss

characterization,

we

generate

a

large

number

of

loss

patterns

by

using

different

seeds

for

the

pseudcerandom

number

generator

(for

the

results

presented

here

we

used

patterns

for

each

simulated

condition

for

a

single

speech

sample).

This

procedure

takes

thus

into

account

that

the

impact

of

loss

on

an

input

signal

may

not

be

homogenous

(i.e.,

a

loss

burst

within

one

segment

of

that

signal

can

have

a

different

perceptual

impact

than

a

loss

burst

of

the

same

size

within

another

segment).

By

averaging

the

result

of

the

objective

quality

measure

for

several

loss

patterns,

we

have

a

reliable

indication

for

the

performance

of

the

codec

operating

under

a

certain

network

loss

condition.

We

employed

a

Gilbert

model

(Fig.

as

the

network

model

for

the

simulations,

as

we

have

found

that

here

higher

order

models

do

not

provide

much

additional

information.

Temporal

sensitivity

We

first

analyze

the

behaviour

for

~-law

PCM

flows

kbit/s)

with

and

without

loss

concealment,

where

the

loss

concealment

repairs

isolated

losses

only

(speech

stationarity

can

only

be

assumed

for

small

time

intervals).

Results

are

shown

for

the

AP/C

concealment

algorithm

([ll]).

Similar

results

were

obtained

with

other

concealment

algorithms.

Figure

shows

the

case

without

loss

concealment

enabled

where

Gilbert

model

parameters

are

varied.

The

resulting

speech

quality

is

insensitive

to

the

loss

distribution

parameter

(elp).

The

results

are

even

slightly

decressing

for

an

increasing

clp,

pointing

to

a

significant

variability

of

theresuits.

In

Figure

the

results

with

loss

concealment

are

depicted.

When

the

loss

correlation

(dp)

is

low,

loss

concealment

provides

a

significant

performance

improvement.

The

relative

improvement

increases

with

increasing

loss

(pulp).

For

higher

clp

values

the

cases

with

and

without

concealment

become

increasingly

similar

and

show

the

same

performance

at

clp

x

Figures

and

respectively

also

contain

a

curve

showing

the

performance

under

the

assumption

of

random

losses

(Bernouilli

model,

ulp

elp).

Thus,

considering

a

given

ulp,

a

worst

case

loss

pattern

of

alternating

losses

(I(s

mod

l,l([s

l]

mod

would

enable

a

considerable

performance

improvement

(with

ok

OVk

p~,cond

Eq.

As

we

found

by

visual

inspection

that

the

distributions

of

the

perceptual

distortion

values

for

one

loss

condition

seem

to

approximately

follow

a

normal

distribution

we

employ

mean

and

standard

deviation

to

describe

the

statistical

variability

of

the

measured

values.

Figure

presents

the

perceptual

distortion

as

in

the

previous

figure

but

also

give

the

standard

deviation

as

error

bars

for

the

respective

loss

condition.

It

shows

the

increasing

variability

of

the

results

with

increasing

loss

correlation

(clp),

while

the

variability

does

not

seem

to

change

much

with

an

increasing

amount

of

loss

(alp).

On

one

hand

this

points

to

some,

though

weak,

sensitivity

with

regard

to

heterogeneity

(i.e.,

it

matters

which

area

of

the

speech

signal

(voiced/unvoiced)

experiences

a

burst

loss).

On

the

other

hand

it

shows,

that

a

large

number

of

different

Figure

Utility

curve

for

PCM

without

loss

conc

e

a

l

m

e

n

t

(EMBSD)

Figure

Utility

curve

for

PCM

with

loss

concealment

(EMBSD)

Figure

Variability

of

the

perceptual

distortion

with

concealment

(EMBSD)

Figure

Utility

curve

for

the

codec

(EMBSD)

loss

patterns

is

necessary

for

a

certain

speech

sample

when

using

objective

speech

quality

measurement

to

assess

the

impact

of

loss

corwlation

on

user

perception.

uses

the

Conjugate

Structure

Algebraic

Code

Excited

Linear

Prediction

(CS-ACELP)

coding

scheme

and

ooerates

at

kbit/s.

In

a

speech

fmme

is

ms

in

d&ion.

For

each

frame,

encoder

analyzes

the

input

data

and

extracts

the

parameters

of

the

Code

Excited

Linear

Prediction

(CELP)

model

such

as

linear

prediction

filter

coefficients

and

excitation

vectors.

When

a

frame

is

lost

or

corrupted,

the

decoder

uses

the

parameters

of

the

previous

frame

to

interpolate

those

of

the

lost

frame.

The

line

spectral

pair

coefficients

of

the

last

good

frame

are

repeated

and

the

gain

coefficients

are

taken

from

the

previous

frame

but

they

are

damped

to

gradually

reduce

their

impact.

When

a

frame

loss

occurs,

the

decoder

cannot

update

its

state,

resulting

in

a

divergence

of

encoder

and

decoder

state.

Thus,

errors

are

not

only

introduced

during

the

time

period

represented

by

the

current

frame

but

also

in

the

following

ones.

In

addition

to

the

impact

of

the

missing

codewords,

distortion

is

increased

by

the

missing

update

of

the

predictor

filter

memories

for

the

line

speo

tral

pairs

and

the

linear

prediction

synthesis

filter

memo

ries.

Figure

shows

that

for

similar

network

conditions

the

output

quality

of

the

is

worse

than

PCM

with

loss

concealment,

demonstrating

the

compression

versus

quality

tradeoff

under

packet

loss.

Interestingly

the

loss

correlation

(dp

parameter)

has

some

impact

on

the

speech

quality,

however,

the

effect

is

weak

pointing

to

a

certain

robustness

of

the

codec

with

regard

to

the

resiliency

to

consecutive

packet

losses

due

to

the

internal

loss

concealment.

Rosenberg

has

done

a

similar

experiment

showing

that

the

difference

between

the

original

and

the

concealed

signal

with

increasing

loss

burstiness

in

terms

of

a

mean-squared

error

is

significant,

however.

This

demonstrates

the

importance

of

perceptual

metrics

which

are

able

to

include

concealment

‘LSPs

are

another

representation

of

the

linear

prediction

coefficients.

‘Two

frames

are

contained

in

a

packet.

Figure

Resynchronization

time

(in

frames)

of

the

decoder

after

the

loss

of

k

consecutive

frames

(k

E

as

a

function

of

frame

position.

(and

not

only

reconstruction)

operations

in

the

quality

assessment.

Sensitivity

due

to

ADU

heterogeneity

PCM

is

a

memoryless

encoding.

Therefore

the

ADU

content

is

only

weakly

heterogeneous

(Figure

Thus,

in

this

section

we

concentrate

on

the

coda.

The

experiment

we

carry

out

is

to

meawre

the

resynchronization

time

of

the

d*

coder

after

k

consecutive

frames

are

lost.

The

decoder

is

said

to

have

resynchronized

with

the

encoder

when

the

energy

of

the

error

signal

falls

below

one

percent

of

the

energy

of

the

decoded

signal

without

frame

loss

(this

is

equivalent

to

a

signal-t-noise

ratio

(SNR)

threshold

of

The

error

signal

energy

(and

thus

the

SNR)

is

computed

on

a

per-frame

basis.

Figure

shows

the

resynchronization

time

(expressed

in

the

number

of

frames

needed

until

the

threshold

is

exceeded)

plotted

against

the

position

of

the

loss

(i.e.,

the

index

of

the

first

lost

frame)

for

d&rent

values

of

k.

The

speech

sample

is

produced

by

a

male

speaker

where

an

unvoiced/voiced

(au)

transition

occurs

in

the

eighth

frame.

We

can

see

from

Figure

that

the

position

of

a

frame

loss

has

a

signilicant

inlluence

cm

the

resulting

signal

degradation",

while

the

degradation

is

not

that

sensitive

to

the

length

of

the

frame

loss

burst

k.

The

loss

of

unvoiced

frames

seems

to

have

a

rather

small

impact

on

the

signal

degradation

and

the

decoder

recovers

the

state

information

fast

thereafter.

The

loss

of

voiced

frames

causes

a

larger

degradation

of

the

speech

signal

and

the

decoder

needs

more

time

to

resyncbronize

with

the

sender.

However,

the

loss

of

voiced

frames

at

an

unvoiced/voiced

transition

leads

to

a

significant

degradation

of

the

signal.

We

have

repeated

the

experiment

for

different

male

and

female

speakers

and

obtained

similar

results.

lsking

into

account

the

wed

coding

scheme,

the

above

phenomenon

could

be

explained

as

follows:

Because

voiced

sounds

have

a

higher

energy

than

unvoiced

sounds,

the

loss

of

voiced

frames

causes

a

larger

signal

degradation

than

the

loss

of

unvoiced

frames.

However,

due

to

the

periodic

property

of

voiced

sounds,

the

decoder

can

conceal

‘While

on

one

hand

we

see

that

SNR

n~easu~es

often

do

not

correlate

well

with

subjective

speech

quality,

on

the

other

hand

the

large

differences

in

the

SNR-threshold-based

resynchronization

time

clearly

point

to

a

significant

impact

on

subjective

speech

quality.

Figure

Differential

RED

drop

probabilities

as

a

function

of

average

queue

sizes

the

loss

of

voiced

frames

well

once

it

has

obtained

suflicient

information

on

them.

The

decoder

fails

to

conceal

the

loss

of

voiced

frames

at

an

unvoiced/voiced

transition

because

it

attempts

to

conceal

the

loss

of

voiced

frames

using

the

filter

coefficients

and

the

excitation

for

an

unvoiced

sound.

Moreover,

because

the

encoder

uses

a

moving

average

filter

to

predict

the

values

of

the

line

spectral

pairs

and

only

transmits

the

difference

between

the

real

and

predicted

vaues,

it

takes

a

lot

of

time

for

the

decoder

to

resynchronize

with

the

encoder

once

it

has

failed

to

build

the

appropriate

linear

prediction

filter.

QUEUE

MANAGEMENT

FOR

INTRAFLOW

LOSS

CONTROL

While

we

have

highlighted

the

sensitivity

of

VoIP

traffic

to

the

distribution

of

loss

in

the

last

sections,

we

now

want

to

briefly

introduce

a

queue

management

mechanism

which

is

able

to

enforce

the

relative

preferences

of

the

ap

plication

with

regard

to

loss.

We

consider

flows

with

packets

marked

with

+l

and

(as

described

in

the

introduction)

BS

foreground

traffic

(FT)

and

other

(best

effort)

flows

as

backaound

traffic

(BT).

Packet

marking,

in

addition

to

keeping

the

desirable

property

of

state

aggregration

within

the

network

core

as

proposed

by

the

IETF

Differentiated

Services

architecture,

is

exploited

here

to

convey

the

intrbflow

requirements

of

a

llow.

As

it

should

be

avoided

to

introduce

reordering

of

the

packets

of

a

flow

in

the

network

we

consider

mechanisms

for

the

management

of

a

single

queue

with

different

priority

levels.

One

approach

to

realize

inter-flow

service

differentiation

using

a

single

queue

is

RIO

(‘RED

with

IN

and

OUT",

With

RIO,

two

average

queue

sizes

as

congestion

indicators

are

computed:

one

just

for

the

IN

(high

priority)

packets

and

another

for

both

IN

and

OUT

(low

priority)

packets.

Packets

marked

as

OUT

are

dropped

earlier

(in

terms

of

the

average

queue

size)

than

IN

packets.

RIO

has

been

designed

to

decrease

the

clip

seen

by

particular

Bows

at

the

expense

of

other

flows.

In

this

work,

however,

we

want

to

keep

the

ulp

as

given

by

other

parameters

while

modifying

the

loss

distribution

for

the

foreground

traffic

(FT).

This

amounts

to

trading

the

loss

of

a

+l

packet

against

a

packet

of

the

same

flow

(in

a

statistical

sense).

Fig.

shows

the

conventional

RED

drop

probability

curve

(po

as

a

function

of

the

average

queue

size

for

all

arrivals

avg),

which

is

applied

to

all

unmarked

traffic

(background

traffic:

BT).

The

necessary

relationship

between

the

drop

probabilities

for

packets

marked

as

and

+l

can

be

derived

follows

(note

that

this

relationship

is

valid

both

at

the

end-tend

level

and

every

individual

hop):

Let

a

a+,

+a-~

be

the

overall

number

of

emitted

packets

by

an

FT

flow

and

a,,

E

[-l,O,

+l]

be

the

number

of

packets

belonging

to

a

certain

class

(where

the

class

corresponds

to

(unmarked)

best

effort

traflic).

Then,

with

a+,

a,~,

and

considering

that

the

resulting

service

has

to

be

best

effort

in

the

long

term,

we

have:

aopo

a+lp+l

A

ape

Qlll@+l

(a--oo)m

P

l

Due

to

this

relationship

between

the

drop

probability

for

+l

and

packets,

we

designate

this

queue

management

algorithm

as

Differential

RED

(DiERED).

Figure

shows

the

corresponding

drop

probability

curves.

Due

to

the

condition

of

a+,

(I-L

all,

in

addition

to

the

conventional

RED

behaviour,

the

DiffRED

implementation

should

also

monitor

the

+l

and

arrival

processes.

If

the

ratio

of

+l

to

packets

at

a

gateway

is

not

(either

due

to

misbehaving

flows

or

a

significant

number

of

flows

which

have

already

experienced

loss

at

earlier

hops)

the

loss

prob

ability

is

decreased

and

the

+l

probability

is

increased

at

the

same

time

thus

degrading

the

service

for

all

users.

The

shaded

areas

above

and

below

the

po(avg)

curve

(Fig.

show

the

operating

area

when

this

correction

is

added.

In

it

has

been

shown

that

using

only

the

conventional

RED

average

queue

size

avg

for

DSRED

operation

is

not

sufficient.

This

is

due

to

the

potentially

missing

correlation

of

the

computed

aug

value

between

consecutive

+l

and

arrivals,

especially

when

the

share

of

the

FT

traftic

is

low.

As

this

might

result

in

a

unfair

distribution

of

losses

between

the

FT

and

BT

fractions,

a

specific

avgl

value

is

computed

by

sampling

the

queue

size

only

at

FT

arrival

instants.

Thus,

a

service

differentiation

for

foreground

traffic

is

possible

which

does

not

differ

from

conventional

RED

behaviour

in

the

long

term

average

(i.e.,

in

the

ulp).

INTRA-FLOW

LOSS

RECOVERY

AND

CONTROL

Temporal

sensitivity

Considering

a

flow

with

temporal

loss

sensitivity,

paragraph

has

shown

that

a

simple,

periodic

loss

pattern

enhances

the

performance

of

the

end-to-end

loss

recovery.

The

pattern

is

not

tied

to

particular

packets,

therefore

a

per-flow

characterization

with

the

introduced

metrics

is

applicable.

In

this

paragraph

we

assume

that

a

flow

expressed

its

temporal

se*sitivity

by

marking

its

flow

with

an

alternating

pattern

of

,c+l,>,‘c~l,,,

Figure

Comparison

of

actual

and

estimated

Figure

Comparison

of

actual

and

estimated

burst

loss

length

rates

as

a

function

of

burst

length

burst,

loss

length

rates

as

a

function

of

burst

length

k:

three

state

run-length-based

model

k:

two

state

run-length-based

model

(Gilbert.)

Figures

and

show

the

rates

for

the

actual

and

the

estimated

burst

loss

lengths

for

a

three-state

(m

and

a

two=state

(m

Gilbert)

model

We

can

observe

that

DiffRED

shapes

the

burst

probability

curve

in

the

desired

way.

Most

of

the

probability

mass

is

concentrated

at,

isolated

losses

(the

ideal

behaviour

would

be

the

occurence

of

only

isolated

losses

(Ic

which

can

be

expressed

with

clp

in

terms

of

Gilbert

model

parameters).

With

Drop

Tail

an

approximately

geometrically

decreasing

burst

loss

probability

with

increasing

burst

length

(Eq.

is

obtainable,

where

the

clp

parameter

is

relatively

large

though.

Thus,

considering

voice

with

temporal

loss

sensitivity

as

the

foreground

traffic

of

interest,

with

DifFRED

a

large

number

of

short

annoying

bursts

can

be

traded

against

a

larger

number

of

isolated

losses

and

few

long

loss

bursts

(which

occur

when

the

queue

is

under

temporary

overload,

i.e.,

awg

maxth,

Fig.

We

can

see

that

the

three-state

model

estimation

(Eq.

reflects

the

two

areas

of

the

DifFRED

operation

(the

sharp

drop

of

the

burst

loss

length

rate

for

k

and

the

decrease

along

a

geometrically

decreasing

asymptote

for

k

This

effect

cannot

be

captured

by

the

two

state

model

(Eq.

which

thus

overestimates

the

burst

loss

length

rate

for

Ic

and

then

hugely

underestimates

it

for

k

Interestingly,

for

Drop

Tail,

while

both

models

capture

the

shape

of

the

actual

curve,

the

lower

order

model

is

more

accurate

in

the

estimation.

This

can

be

explained

as

follows:

if

the

burst

loss

length

probabilities

are

in

fact

close

to

a

geometrical

distribution,

the

estimate

is

more

robust

if

all

data

is

included

(note

that

the

run-length

based

approximation

of

the

conditional

loss

probability

P(X

mlX

m)

only

includes

loss

run-length

occurences

larger

or

equal

to

m:

Eq.

sWe

only

discuss

the

results

qualitatively

here

to

give

an

example

how

an

intra-flow

loss

control

algorithm

performs

and

to

show

how

loss

models

can

capture

this

performance.

Details

on

the

simulation

scenario

and

parameters

can

be

found

in

Sensitivity

due

to

ADU

heterogeneity

In

paragraph

we

have

seen

that

sensitivity

to

ADU

heterogeneity

results

in

a

certain

non-periodic

loss

pattern.

Thus,

a

mechanism

at

(or

near)

the

sender

is

necessary

which

derives

that

pattern

from

the

voice

data.

Furthermore,

an

explicit

cooperation

between

end-to-end

and

hop

by-hop

mechanisms

is

necessary

(Fig.

We

use

the

result

of

paragraph

to

develop

a

new

packet

marking

scheme

called

Speech

Property-Based

Selective

Differential

Packet

Marking

(SPB-DIFFMARK).

The

DIFFMARK

scheme

concentrates

the

higher

priority

packets

on

the

frames

essential

to

the

speech

signal

and

relies

on

the

decoder"s

concealment

for

other

frames.

Figure

shows

the

simple

algorithm

written

in

a

pseudocode

that

is

used

to

detect

an

unvoiced/voiced

(uw)

transition

and

protect

the

voiced

frames

at,

the

beginning

of

a

voiced

signal.

The

procedure

is

used

to

classify

a

block

of

Ic

frames

as

voiced,

unvoiced,

or

uv

transition.

is

used

to

send

a

block

of

Ic

frames

as

a

single

packet

with

the

appropriate

priority

(either

fl,

or

As

the

core

algorithm

gives

only

a

binary

marking

decision

(protect

the

packet

or

not),

we

employ

a

simple

algorithm

to

send

the

necessary

packets

for

compensation

(Eq.

after

a

burst

of

+l

packets

has

been

sent,

a

corresponding

number

of

packets

is

sent

immediately.

State

about

the

necessary

number

of

to-be-sent

packets

is

kept

in

the

event

that

the

SPB

algorithm

triggers

the

next

+l

burst

before

all

packets

necessary

for

compensation

are

sent.

Thus,

seen

over

time

intervals

which

are

long

compared

to

the

burst

times,

the

mean

loss

for

the

flow

will

be

equal

to

the

best

effort

case

(Eq.

N

is

a

pre-defined

value

and

defines

how

many

frames

at

the

beginning

of

a

voiced

signal

are

to

be

protected.

Our

simulations

(Fig.

have

shown

that

the

range

from

to

are

appropriate

values

for

N

(depending

on

the

network

loss

condition).

In

the

simulations

presented

below,

we

choose

Ic

a

typical

protect

fcm?ach

(k

frames)

classify

analysis(k

frames)]

if

(protect

if

(classify

unvoiced)

protect

if

(compensation

compensation

compensation-k

send(k

frames,

else

send(k

frames,

endif

else

send(k

frames,

+l)

protect

protect-k

compensation

compensation+k

endif

else

if

(classify

uvfransition)

send(k

frames,

+l)

protect

N

k

compensation

compensation+k

else

if

(compensation

compensation

compensation-k

send(k

frames,

else

send(k

frames,

endif

endif

endif

endfor

Figure

SPB-DIFFMARK

Pseudo

Code

value

for

interactive

speech

transmissions

over

the

Internet

of

audio

data

per

packet).

A

larger

number

of

Ic

would

help

to

reduce

the

relative

overhead

of

the

protocol

header

but

also

increases

the

packetization

delay

and

makes

sender

classification

and

receiver

concealment

in

case

of

packet

loss

(due

to

a

larger

loss

gap)

more

difficult.

En.d-to-end

simulation

description

Due

to

the

non-periodic

loss

pattern,

we

need

to

explicitly

associate

a

drop

probability

with

a

single

packet

within

an

end-to-end

model.

Therefore

we

use

a

separate

one-state

Markov

model

(Bernouilli

model)

to

describe

the

network

behaviour

as

seen

by

each

class

of

packets.

Best

effort

packets

(designated

by

in

Fig.

are

dropped

with

the

probability

pc,

whereas

packets

marked

with

+l

and

are

dropped

with

probabilities

of

p+i

and

respectively.

This

is

a

reasonable

assumption"

with

regard

to

the

interdependence

of

the

different

classes

in

fact,

as

it

has

been

shown

that

DiffRED

(Figs.

and

achieves

a

fair

amount

of

decorrelation

of

+l

and

packet

losses.

Nevertheless

to

include

some

correlation

between

the

classes

we

have

set

p+i

pc

for

the

subsequent

simulations.

This

should

‘The

appropriateness

of

the

simple

end-to-end

modeling

used

has

been

investigated

in

with

discrete

event

simulation

using

a

multi-hop

topology

and

detailed

modeling

of

foreground

and

background

traffic

sources.

Marking

Scheme

Network

Model

N

O

M

A

R

K

lolo/

F

U

L

L

M

A

R

K

+I

+I

+I

+I

+l

+I

SPB

MARK

j

+I

I

I

ALT

MARK

Ol+ll

+I

Pcl

po

S

P

B

D

I

F

F

M

A

R

K

IJ

+l

I

+l

I

I

ALT

DIFFMARK

+I

/-I

Figure

Marking

schemes

and

corresponding

network

models.

also

allow

a

reasonable

evaluation

of

how

losses

in

the

fl

class

affect

the

performance

of

the

SPB-algorithms.

For

a

direct

comparison

with

SPB-DIFFMARK,

we

evaluate

a

scheme

where

packets

are

alternatingly

marked

as

being

either

or

+l

(ALT-DIFFMARK,

Figure

We

furthermore

include

related

inter-flow

loss

protection

schemes.

The

first

scheme

uses

full

protection

(FULL

MARK,

all

packets

are

marked

as

+l).

The

SPB-MARK

scheme

operates

similarly

to

SPB-DIFFMARK,

but

no

packets

are

sent

for

compensation

(those

packets

are

also

marked

as

For

comparison

we

again

use

a

scheme

where

packets

are

alternatingly

marked

as

being

either

or

+l

(ALT-MARK).

Finally,

packets

of

pure

best

effort

flows

are

dropped

with

the

probability

po

(NO

MARK

case

in

Fig.

For

the

SPB

marking

schemes

the

percentage

of

+l-

and

-l-marked

packets

respectively

is

for

the

speech

material

used.

We

obtained

similar

marking

percentages

for

other

speech

samples.

The

ALT

marking

schemes

mark

exactly

of

their

packets

as

being

fl.

Results

Figure

shows

the

perceptual

distortion

for

the

marking

schemes

dependent

on

the

drop

probability

pc.

The

unprotected

case

(NO

MARK)

has

the

highest

perceptual

distortion

and

thus

the

worst

speech

quality*.

The

differential

marking

scheme

(SPB-DIFFMARK)

offers

a

significantly

better

speech

quality

even

when

only

using

a

network

service

which

amounts

to

best

effort

in

the

long

term.

Note

that

the

ALT-DIFFMARK

marking

strategy

does

not

differ

from

the

best

effort

case

(which

confirms

the

result

of

paragraph

SPB-DIFFMARK

is

also

even

better

than

the

inter-flow

QoS

ALT-MARK

scheme,

especially

for

higher

values

of

pe.

These

results

validate

the

strategy

of

our

SPB

marking

schemes

that

do

not

equally

mark

all

packets

with

a

higher

priority

but

rather

protect

a

subset

of

frames

that

are

essential

to

the

speech

quality.

The

SPB-FEC

scheme

*We

have

also

perfo

rmed

informal

listening

tests

which

confirmed

the

results

using

the

objective

metrics.

Figure

Perceptual

Distortion

(EMBSD)

for

the

marking

schemes

and

SPB-FEC

which

uses

piggybacked

on

the

main

payload

packets

(RFC

to

protect

a

subset

of

the

packets,

enables

a

very

good

output

speech

quality

for

low

loss

rates.

However,

it

should

be

noted

that

the

amount

of

data

sent

over

the

network

is

increased

by

Note

that

the

simulation

presumes

that

this

additionally

consumed

bandwidth

itself

does

not

contribute

significantly

to

congestion.

This

assumption

is

only

valid

if

a

small

fraction

of

trafhc

is

voice

([S]).

The

SPB-FEC

curve

is

convex

with

increasing

po,

as

due

to

the

increasing

loss

correlation

an

increasing

number

of

consecutive

packets

carrying

redundancy

are

lost

leading

to

unrecoverable

losses.

The

curve

for

SPB-DIFFMARK

is

concave,

however,

yielding

better

performance

for

pe

The

inter-flow

QoS

ALT-MARK

scheme

of

the

packets

are

marked)

enhances

the

perceptual

quality.

However,

the

auditory

distance

and

the

perceptual

distortion

of

the

SPB-MARK

scheme

(with

of

all

packets

marked)

is

significantly

lower

and

very

close

to

the

quality

of

the

decoded

signal

when

all

packets

are

marked

(FULL

MARK).

This

also

shows

that

by

protecting

the

entire

flow

only

a

minor

improvement

in

the

perceptual

quality

is

obtained.

The

results

for

the

FULL

MARK

scheme

also

show

that,

while

the

loss

of

some

of

the

+l

packets

has

some

measurable

impact,

the

impact

on

perceptual

quality

can

still

be

considered

to

be

very

low.

CONCLUSIONS

In

this

paper

we

have

characterized

the

behaviour

of

a

samplebased

codec

(PCM)

and

a

frame-based

codec

in

the

presence

of

packet

loss.

We

have

then

developed

intraflow

loss

recovery

and

control

mechanisms

to

increase

the

perceptual

quality.

While

we

have

tested

other

codecs

only

informally,

we

think

that

our

results

reflect

the

fundamental

difference

between

codecs

which

either

encode

the

speech

wave‘We

also

used

the

encoder

for

the

redundant

source

coding.

form

directly

or

which

are

based

on

linear

prediction.

For

PCM

without

loss

concealment

we

have

found

that

it

neither

exhibits

significant

temporal

sensitivity

nor

sensitivity

to

payload

heterogeneity.

With

loss

concealment,

however,

the

speech

quality

is

increased

but

the

amount

of

increase

exhibits

strong

temporal

sensitivity.

Frame-based

codecs

amplify

on

one

hand

the

impact

of

loss

by

error

propagation,

though

on

the

other

hand

such

coding

schemes

help

to

perform

loss

concealment

by

extrapolation

of

decoder

state.

Contrary

to

sample-based

codecs

we

have

shown

that

the

concealment

performance

of

the

decoder

may

break

at

transitions

within

the

speech

signal

however,

thus

showing

strong

sensitivity

to

payload

heterogeneity.

We

have

briefly

introduced

a

queue

management

algorithm

which

is

able

to

control

loss

patterns

without

changing

the

amount

of

loss

and

characterized

its

performance

for

the

loss

control

of

a

flow

exhibiting

temporal

sensitivity.

Then

we

developed

a

new

packet

marking

scheme

called

Speech

Property-Based

Selective

Differential

Packet

Marking

for

an

efficient

protection

of

frame-based

codecs.

The

SPBDIFFMARK

scheme

concentrates

the

higher

priority

packets

on

the

frames

essential

to

the

speech

signal

and

relies

on

the

decoder"s

concealment

for

other

frames.

We

have

also

evaluated

the

mapping

of

an

end-to-end

algorithm

to

interflow

protection.

We

have

found

that

the

selective

marking

scheme

performs

almost

as

good

as

the

protection

of

the

entire

flow

at

a

significantly

lower

number

of

necessary

highpriority

packets.

Thus,

combined

intra-flow

end-to-end

hopby-hop

schemes

seem

to

be

well-suited

for

heavily-loaded

networks

with

a

relatively

large

fraction

of

voice

traffic.

This

is

the

case

because

they

neither

need

the

addition

of

redundancy

nor

feedback

(which

would

incur

additional

data

and

delay

overhead)

and

thus

yield

stable

voice

quality

also

for

higher

loss

rates

due

to

absence

of

FEC

and

feedback

loss.

Such

schemes

can

better

accomodate

codecs

with

fixed

output

bitrates,

which

are

difficult

to

integrate

into

FEC

schemes

requiring

adaptivity

of

both

the

codec

and

the

redundancy

generator.

Also,

it

is

useful

for

adaptive

codecs

running

at

the

lowest

possible

bitrate.

Avoiding

redundancy

and

feedback

is

also

interesting

in

multicast

conferencing

scenarios

where

the

end-to-end

loss

characteristics

of

the

different

paths

leading

to

members

of

the

session

are

largely

different.

Our

work

has

clearly

focused

on

linking

simple

end-to-end

models

which

can

be

easily

parametrized

with

the

known

characteristic

of

hopby-hop

loss

control

to

user-level

metrics.

An

analysis

of

a

large

scale

deployment

of

non-adaptive

or

adaptive

FEC

as

compared

to

a

deployment

of

our

combined

scheme

requires

clearly

further

study.

ACKNOWLEDGMENTS

We

would

like

to

thank

Wonho

Yang

and

Robert

Yantorno,

Temple

University,

for

providing

the

EMBSD

software

for

the

objective

speech

quality

measurements.

Michael

Zander,

GMD

Fokus,

helped

with

the

simulations

of

the

queue

management

schemes.

ADDITIONAL

AUTHORS

Additional

author:

Georg

Carle

(GMD

Fokus,

email:

carle@fokus.gmd.de).

PI

PI

PI

PI

PI

PI

WI

WI

w

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O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

network support for real-time multimedia

loss recovery and control
loss sensitivity
quality of service
voip traffic
general markov model
queue management algorithm
loss concealment
objective speech quality measurement
queue management