Adaptive Video Encoding and Dynamic Channel
Access for Real-time Streaming over SDRs
Debashri Roy∗ , Tathagata Mukherjee† , Mainak Chatterjee∗ , Eduardo Pasiliao‡∗ Computer Science† Computer Science‡ Munitions Directorate

University of Central Florida
Orlando, FL 32826{debashri, mainak}@cs.ucf.edu

University of Alabama
Huntsville, AL 35899{tm0130}@uah.edu

Air Force Research Laboratory
Eglin AFB, FL, 32542{eduardo.pasiliao}@us.af.mil

Abstract—In this paper we study and implement real-time
adaptation schemes for video encoding and channel selectionthat work in tandem to facilitate HD video streaming forsecondary users in a dynamic spectrum access network. Out-ofband feedbacks on instantaneous pathloss of the signal between
the transmitter and the receiver, the received signal strength indicator (RSSI) at the receiver and the quality of the reconstructed
video are used to continuously determine the most apt encoding
parameters. At the same time, the radio transmitter continuously adjusts the channel parameters (i.e., center frequency andchannel bandwidth) based on the transmission activities of the
primary users who have prioritized rights on these channels.
We consider the physical limitations of the encoder along withthe channel statistics to determine when to change the encoder
parameters and when to switch to a new channel. We propose
a multi-level threshold based mechanism to find the optimal
number of encoding bit rates. We also propose a thresholdbased algorithm to find the best available channel between thetransmitter-receiver pair. We validate our theoretical propositions
on an indoor testbed using software defined radios (SDRs) and
the GNU Radio suite. Live video was captured, encoded using
open source H.264 software libraries, streamed using GStreamer
and transmitted over the 915 MHz ISM bands with omnidirectional antennas. For the SDRs, we chose the universal
software radio peripheral (USRP) B210s from Ettus Researchand use them as the transmitter and the receiver. A third B210
was used to sense the energy levels on all the channels to detect
the presence of primary transmissions. GNU Radio was used
to build the signal processing pipeline, both for the transmitterand the receiver. We use PSNR and SSIM to measure the video
quality and report experimental results that show that: (i) thevideo encoder and the USRP transmitter-receiver pair are able
to adapt to the changing RF conditions, (ii) the adaptation
schemes yield better video quality than non-adaptive schemes,and (iii) the USRPs can switch the channels fast enough allowing
uninterrupted HD video streaming even when primary users
preempt the secondary user’s transmission.

I. I NTRODUCTION
With the increasing demand for high definition (HD) video
over wireless access networks, the wireless service providers
are finding it difficult to handle the large volumes of the video
traffic [1]. In recent years, the research community on video
processing and delivery has contributed towards implementing
sophisticated encoding techniques that ensure minimal storage
and are resilient against transmission losses [2], [3]. Results
have shown that knowledge about network status and coordination among users that share resources result in better encodingc
978-1-5386-6808-5/18/$31.00 ⃝2018
IEEE

decisions and overall utilization of the bandwidth. In spite ofthese developments, guaranteeing QoS over wireless networks
has always been a challenge due to the unpredictable channel
characteristics [4]. The problem is even more aggravated forreal-time video transmission because of the strict inter-packet
delay and bandwidth requirements. Alongside, to alleviate theproblem of bandwidth shortage in some of the spectrum bands,
the concept of dynamic spectrum access (DSA) has been
proposed and implemented [5], the objective of which is toefficiently manage the radio spectrum and still provide thequality of service (QoS) that the end users expect.
In a DSA network, secondary users (i.e., unlicensed users)
typically equipped with SDRs continuously monitor the presence of primary users (i.e., licensed users) and opportunistically access the unused or under-utilized licensed bands ofprimaries [6]. SDRs define different hardware components
through software implementations which can be programmed
to continuously adapt to the changing RF environment by adjusting the waveforms, modulation schemes, filters, and otherRF parameters. Technologies based on the DSA paradigm provide some relief to the bandwidth shortage problem; however
it is yet to be seen what kind of data-intensive applications
can the software defined radios handle. In particular, if theycan efficiently handle HD video transmissions in a contested
environment. Also, it is yet to be seen how well and how
fast can the radios adapt to the changing network and channel
conditions.
One of the motivations of this work is to investigate to what
extent can video source coding and wireless transmissions be
jointly optimized, given the network latencies and hardware
constraints. Though there are some work that deal with joint
source and channel coding [2] [7], they are mostly confinedto the theoretical domain. Also, RF parameter adaptations for
DSA systems do not consider the QoS vector of the application
being supported [8], [6]. We not only provide a theoreticalgroundwork for joint video coding and channel selection formaintaining QoS, but also implement them on video encoders
and USRPs (a software defined radio manufactured by Ettus
Research) to demonstrate their practical feasibility.
In this paper, we propose and implement adaptive video encoding and channel adaptation schemes on an indoor testbed.
Using an out-of-band real-time feedback from the receiver
about the video quality, the encoder continuously adapts theencoding bitrate. Also, the transmission parameters, particularly the center frequency and the channel bandwidth, arechanged based on the SINR at the receiver and the transmission activities of the primary users. The objective of the
proposed adaptation techniques is to keep the video streaming
uninterrupted by changing the source coding and channel
parameters, based on QoS at the receiver. Our novelty lies inhaving the H.264 codec to continuously tune the parametersat any time granularity and for the radio transmitter to change
the channel parameters simultaneously. Though extremely fast
adaptation is desirable and have been proposed in theory [2],[9], in practical scenarios, we are limited by the hardware
capabilities. Thus, instead of changing the encoding rates
at infinitesimally small time scales, we use a multi-level
threshold based mechanism for determining when to change
the encoding rate and what that rate should be. The multi-level
threshold is based on the probability density function (pdf)of the received power at the receiver. Moreover, we tune thetransmitting frequency to the most desirable channel between
the transmitter-receiver pair based on the sensed RSSI at thereceiver. If the current channel is found to degrade the video
quality, the transmitter and receiver switch to the next bestchannel.
Our theoretical propositions have been validated on an
experimental testbed using USRP and GNU Radio. We used
the 902 - 926 MHz ISM band to create 8 channels of 3
MHz each. Live video captured via a webcam was encodedusing open source H.264 software libraries and streamed using
GStreamer [10]. The video transport streams were transmittedusing USRP B210 which used Gaussian Minimum Shift Keying (GMSK) modulation and omni-directional antennas. The
encoding rate and the transmitting frequency were changed
based on the reconstructed video quality measured using
PSNR and SSIM. Results showed that the proposed encoding
and channel adaptation techniques were able to substantially
improve the quality of video displayed at the receiver. To thebest of our knowledge, this paper is the first to propose andimplement source encoding and channel adaptation schemeswith a real-time feedback control mechanism in the DSA
paradigm. Insights from the results reveal that leveraging offthe-shelf USRPs for real-time HD video transmission wouldease the over-burdened radio resources.
The rest of the paper is organized as follows. Related works
with respect to video transmission over wireless networks are
discussed in Section II. System model and video assessment
techniques used are presented in Section III. The proposed approach for video encoding and channel adaptation techniques
are discussed in Section IV. Testbed implementation details aredescribed in Section V. Experimental results are presented in
Section VI. Finally, conclusions are drawn in the last section.
II. R ELATED W ORKThis section summarizes the studies evaluating different
source channel encoding techniques and video quality assessments in the DSA paradigm. Software defined radios haveemerged as a promising solution for solving the spectrum

scarcity problem and improving the spectral efficiency byleveraging the concept of spatial reuse of radio bands [11].
In [12], the authors prove that maximizing the number ofserved multimedia streams in a single-frequency network is
NP-Complete and propose a heuristic algorithm with polynomial running time. Efficient utilization of the radio bands
depends on how well the primary users (PU) and secondaryusers (SU) are able to coordinate their transmission activities.
Many PU and SU activity models have been reported in [13].
In one such study, the primary user activity pattern has been
assumed to follow a Poisson model [14]. However, the Poissonmodel has drawbacks of achieving lower throughput and highfalse-alarm rates. In [15], a parameterized primary user trafficmodel has been proposed by forming cluster of correlated
primary users. In [4], the spectrum opportunities for secondaryusers based on primary users activity patterns was investigated.
In [16], the authors modeled the primary user activity as a
Semi-Markov ON/OFF process.
In [17], the authors proposed three types of channel selection schemes: best-fit channel selection (BFC), longest idle
time channel selection (LITC), and 𝑝-selfish channel selection. The authors proved that non-selfish selection improves
performance over selfish selection schemes. Both PU and SU
activity models were considered to be 3-state Markov chain.
A distributed channel selection scheme was proposed in [18]for efficient data dissemination in a multi-hop cognitive radio
network, based on channel classifications. Authors in [19] tried
to overcome the bandwidth requirement limitation for mobile
videos by proposing a cross-layer design. An experimental
deployment of a DSA system for video transmission using
USRPs and GNU Radios was presented in [6]. It showed DSA
based systems perform better than non-DSA ones. However,this work did not use any video quality metrics as we havedone. A system level solution for a database-assisted DSA
system using inexpensive software configurable RF chips was
proposed in [8].
As far as video adaptation techniques over wireless accessnetworks are concerned, there have been some breakthroughs.
A video quality assessment technique, for different kindsof wireless networks in high mobility environments, waspresented in [20]. In [2], the authors presented a popularityaware joint source-channel coding optimization frameworkfor video plus depth format. They proposed a framework to
generate a rate embedded bitstream which would be optimally
decodable at multiple data rates depending on the diversityof video perspectives. Another joint source-channel coding(JSCC) approach was proposed in [3] for 3-D stereo video
transmission using video plus depth. The authors consideredflat Rayleigh fading to model the noisy channel. In [9], theauthors implemented variable-length codes with feedback to
improve the delay-distortion trade-off and power constraints.
Most of the existing JSCC techniques focus on the various
video coding standards; they do not necessarily consider the
networking and radio parameters. Jointly optimizing source
encoding parameters and RF parameters on a real wirelessplatform for real-time video streaming remains a challengebecause of (i) the limited bandwidth for a large number ofusers; (ii) non-reliable channels; (iii) hardware limitations toadapt changing channel conditions; and (iv) video quality
assurance. Sustaining video quality along with high spectral
efficiency for an over-burdened network is very desirable fromboth video and radio resources’ perspective.
III. S YSTEM M ODEL AND A SSUMPTIONSIn this section, we present the wireless channel models,
and the necessary background on video encoding and quality
assessment techniques that are relevant for the rest of thiswork.
A. Channel Model
A good channel model must incorporate important features
like pathloss, shadowing, and fading. We considered a wirelesschannel with multiple transmitters and receivers, with lineof sight (LOS), primarily to mimic the testbed conditions
discussed later. Next we discuss our assumptions for thewireless channel.
1) Pathloss Model: The received power depends on the
distance between the transmitter and the receiver, denoted by𝑑 and is given by:()2 [ ] 𝛾𝜆𝑑0×(1)
𝑃𝑟 (𝑑) = 𝑃𝑡 ×
4𝜋𝑑0𝑑where 𝑃𝑡 is transmitted power, 𝛾 is the pathloss exponent, 𝜆 is
the wavelength in meters and 𝑑0 is the reference distance forantenna far field which is usually 1 to 10 meters for indoor
environments [11]. The corresponding pathloss in decibel is()[ ]given by:𝜆𝑑0
+ 10𝛾 log
𝑃 𝐿𝑑𝐵 (𝑑) = 20 log
(2)
4𝜋𝑑0
𝑑
2) Shadowing and Fading Model: We assume that signals
are affected by shadowing and fading that capture the multipath effect for wireless channels. The received power envelop(𝑃𝑠 ) due to shadowing is a random variable, the logarithm ofwhich is a zero-mean Gaussian distribution [11] with density()
function given by:1(𝑙𝑛(𝑃𝑠 ))2𝑓 (𝑙𝑛(𝑃𝑠 )) = √
exp −
(3)
2𝛿𝑠2
2𝜋 × 𝛿𝑠2
where 𝛿𝑠2 is the variance of 𝑃𝑠 in 𝑑𝐵 2 .
We use Rician fading which is the most appropriate channel
model for line of sight [21] scenarios. Here the received[𝑟signal amplitude distribution is given by: 𝑃 (𝑟) = 2 exp −
𝛿𝑓
] ( )
𝑟𝑠
𝑟 2 + 𝑠2
𝐼0 2 , where 𝑠 ≥ 0, and 𝑟 ≥ 0. 𝐼0 is the2𝛿𝑓2
𝛿𝑓zero order Bessel function of the first kind, 𝛿𝑓2 is the power
contribution of scattered paths at the received signal, 𝑠 is thepeak amplitude of the dominant path, and 𝑟 is the channel’s
faded amplitude [21].
For a Rician fading channel, the signal amplitude follows
a Gaussian distribution. We assume that the power loss due
to Rician fading, denoted by 𝑥, also follows a Gaussian
distribution with density:(𝑥 − 𝜇𝑑 )2
1
exp −
(4)
𝜓(𝑥) = √
2𝜎𝑠2
2𝜋𝜎𝑠2

where 𝜇𝑑 is the mean powerloss at distance 𝑑, and 𝜎𝑠 is thevariance.
B. Video Encoding
A continuous video stream typically has spatial (similarities
with neighboring pixel blocks), temporal (similarities with prearrived frames), statistical, and perceptual redundancies. The
video codecs implement efficient and pertinent data encoding
by exploiting the different redundancies present in the source
video. Most codecs cannot continuously tune the coding
parameters based on network and channel conditions at any
arbitrarily small time interval. This is because: (i) there is a
finite delay between the moment the receiver sends a feedback
and the moment the encoder acts upon it, and (ii) the hardware
or software based video codecs have their fundamental limits
on operational speeds.
If the channel conditions change during encoder adaptation,
the codec adaptation is not effective. The situation is worse
for fast fading channels. However, adaptation benefits can beachieved for slow fading channels. We consider both slow andfast fading.
C. Video Quality Assessment
Though there are several metrics to measure the video
quality, we use two simple video quality assessments: Peak
Signal to Noise Ratio (PSNR) and Structural Similarity Index(SSIM) [22]. PSNR is the most widely used video quality
metric and SSIM is the most accurate to human perception [22]. PSNR relates to the quality of reconstructed videofrom lossy compression codecs. Higher PSNR generally refers
to better video quality. PSNR values ranging from 30 to 50
represent good quality video with respect to wireless communication [23]. Measurement of SSIM relies on quantizing
structural distortion rather than error. The motivation for thismetric is that human vision responds positively to structuralsimilarity than errors, therefore SSIM gives a better correlation
to the human subjective impression. Good SSIM for wirelesscommunication ranges between 0.8 - 1.0 [24].
IV. P ROPOSED A DAPTATION S CHEMESWe propose two adaptations that work in tandem. The firstone adapts the video encoding parameters in response to thechannel conditions at the receiver such that the video streaming
remains uninterrupted. This is achieved through a feedback
control mechanism from the receiver to the encoder. As forfinding the best encoding rate at any time, we use a multi-level
threshold based mechanism where the thresholds are computed
using the probability density function of the received powerat the receiver. The second adaptation lies in continuously
finding the best channel for the secondary transmitter-receiverpair. As in a DSA based system, when the currently usedchannel is found to be noisy or unusable due to return of the
primary, both the transmitter and the receiver switch to anotherchannel. In Section IV-A, we discuss the proposed encoder
adaptation while in Section IV-B, we present the adaptive
channel selection method.

A. Encoder Adaptation
We assume that the pdf 𝜓(𝑥) (recall that 𝑥 is the power loss)
has a mean of 𝜇𝑑 , and a standard deviation 𝜎𝑠 (∼ [-2.6134 to
2.6134] dB [11]), for indoor LOS. It is intuitive to conclude
that the encoding rate should be decreased (increased) as thechannel conditions deteriorate (improve). However it is not
immediately clear: (i) how frequently should the encoding
rates be changed, and (ii) how do we quantitatively findthe encoding rates. Our objective here is to adapt the video
encoding parameters based on the channel conditions.
1) Frequency of “Encoding Rate” Changes: Theoreticallythe encoding rate should be changed as frequently as possible
in response to the continuously changing channel condition.
However, the hardware might not be able to handle such
quick transitions. The rate at which the encoding bitrate canbe changed is limited by: i) the hardware capabilities of theencoder, streamer, signal processor and SDR, and ii) the time
lag for executing the feedback loop. Hence we seek to find thefrequency at which the encoding rate must be changed suchthat it is the optimal response to the channel conditions within
the capacity of the underlying hardware.
It must be noted that the rate of change of the encoding
bitrate is dependent on how fast the powerloss is changing.
The mean powerloss (𝜇𝑑 ) at a distance (𝑑) for the distribution
𝜓(𝑥), from equation 4, is( given)by:[ ]𝜆𝑑0
𝜇𝑑 = 20 log+ 10𝛾 log
(5)
4𝜋𝑑0
𝑑
Suppose 𝜀𝑀 𝐼𝑁 and 𝜀𝑀 𝐴𝑋 are the minimum and maximum
encoding bit rates that a given encoder can support. Basedon the distance between the transmitter and receiver, theencoder will use an encoding rate [𝜀𝑚𝑖𝑛 (𝑑), 𝜀𝑚𝑎𝑥 (𝑑)], where
𝜀𝑚𝑖𝑛 (𝑑) and 𝜀𝑚𝑎𝑥 (𝑑) are the minimum and the maximum
encoding bitrates for distance 𝑑, respectively. Of course,
𝜀𝑀 𝐼𝑁 ≤ [𝜀𝑚𝑖𝑛 (𝑑), 𝜀𝑚𝑎𝑥 (𝑑)] ≤ 𝜀𝑀 𝐴𝑋 . We formulate 𝜀𝑚𝑖𝑛 (𝑑)and 𝜀𝑚𝑎𝑥 (𝑑) as: ⎧⎨ 𝜀𝑀 𝐼𝑁for 𝑑 ≤ 1
𝜀𝑀 𝐼𝑁 × 𝜇𝜓(𝑑=1)𝜀𝑚𝑖𝑛 (𝑑) =(6)for 𝑑 > 1⎩
𝜁(𝜇𝑑 )⎧⎨ 𝜀𝑀 𝐴𝑋for 𝑑 ≤ 1
𝜀𝑀 𝐴𝑋 × 𝜇𝜓(𝑑=1)(7)𝜀𝑚𝑎𝑥 (𝑑) =for 𝑑 > 1⎩
𝜁(𝜇𝑑 )[()]
𝜆where 𝜇𝜓(𝑑=1) = 20 log+10𝛾 log 𝑑0 is the power4𝜋𝑑0
loss at a distance of 1 meter. The intuition behind equations 6
and 7 is that the encoding rates could be the best possible in thenear field (i.e., 𝑑 ≤ 1). However, as 𝑑 increases, the channel
cannot sustain a higher bit rate and hence, the bit rate should
be decreased. Though 𝜁(.) could be any increasing functionof 𝜇𝑑 , for the sake of simplicity, we use a linear function for𝜁(.) in our experiments (as discussed in Section V).
Once the range of the encoding rates are obtained (i.e.,[𝜀𝑚𝑖𝑛 (𝑑), 𝜀𝑚𝑎𝑥 (𝑑)]), we want to determine the number of
discrete encoding bitrates in that range. In essence, we seek

to find the quantized encoding rates that are optimized for the
given 𝜓(𝑥).
The Optimization Problem: Suppose, we divide the encoding
bit rate range into 𝐿 partitions depending on 𝜓(𝑥). If 𝐿 is
too large, then the changes in encoding bit rate would be
too frequent (contradicting overall hardware and timing lag
constraints). If 𝐿 is too small, then the changes in encoding
rate would be less frequent, hence video quality would be lessadaptive with respect to the channel conditions. Thus we needto optimize 𝐿 so that we can ensure better video quality which
adapts to the channel conditions and adheres to the hardware
constraints. The discrete encoding rates should not be too closeor too far from each other.
As 𝜓(𝑥) is Gaussian, most of the time the powerloss would
be between 𝜇𝑑 ±𝜎𝑠 . This phenomenon asserts the requirementof a non-uniform partitions for mapping the channel to thesource coding. We propose to partition the density functionsuch∫ ∞ that each partition has the same probability mass. As
𝜓(𝑥) = 1, we seek to find 𝐿 partitions with powerloss−∞
levels 𝑥1 , 𝑥2 ⋅ ⋅ ⋅ , 𝑥𝑖 , ⋅ ⋅ ⋅ , 𝑥𝐿 such that:
∫ 𝑥3
∫ ∞
∫ 𝑥2
1
(8)
𝜓(𝑥) =
𝜓(𝑥) = ⋅ ⋅ ⋅ =𝜓(𝑥) =
𝐿
𝑥1 =−∞
𝑥2
𝑥𝐿
Here, 𝑥1 = −∞ is possible as the powerloss is measured indB, which can be negative. Also the last partition is openended, as powerloss can in theory be ∞.
Let us assume 𝜐𝑀 𝐼𝑁 and 𝜐𝑀 𝐴𝑋 are the minimum andmaximum desired gap between two encoding rates, which
are fixed for a given encoder. The encoding rate gap (𝜐𝑖 ) is
calculated as:()
𝜀𝑚𝑎𝑥 (𝑑) − 𝜀𝑚𝑖𝑛 (𝑑) 𝑥𝑖 − 𝜇𝑑×𝜐𝑖 =, 2 ≤ 𝑖 ≤ (𝐿−1) (9)𝐿−2𝜎𝑠
It is to be noted, that 𝜐𝑀 𝐼𝑁 will prevent large 𝐿 and 𝜐𝑀 𝐴𝑋
will prevent small 𝐿. Thus, we get a range for the values of
𝐿 that satisfies()
𝜀𝑚𝑎𝑥 (𝑑) − 𝜀𝑚𝑖𝑛 (𝑑) 𝑥𝑖 − 𝜇𝑑×𝜐𝑀 𝐼𝑁 ≤
≤ 𝜐𝑀 𝐴𝑋 (10)𝐿−2𝜎𝑠Though any value of 𝐿 within the range would suffice,we seek the minimum 𝐿 so as to minimize the frequency
of encoding rate changes that still adapts to the channel
fluctuations. Thus, we can formulate the problem as the aconstrained optimization problem as follows:
minimize∀𝑖(L)

subject to 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 8
𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 10 for 2 ≤ 𝑖 ≤ (𝐿 − 1)This objective function can be solved by non-linear integer
programming (NIP) to get the optimal 𝐿 for a given 𝜓(𝑥) andthe hardware limitations. However, solving NIP is a NP Hard
problem [25]. Since we have some intuition for the value of
𝐿 (i.e., less than 20), we simply try all values of 𝐿 startingfrom 2 for fast processing.
Note that even if we were to change the other encoding
parameters, our formulations would still remain the same. Inthat case 𝜀𝑀 𝐼𝑁 , 𝜀𝑀 𝐴𝑋 , 𝜐𝑀 𝐼𝑁 , and 𝜐𝑀 𝐴𝑋 will have to be
updated based on the parameters selected.
2) Quantitatively Finding the Encoding Rates: Once 𝐿 isknown, we want to determine the exact values for the different
encoding bitrates for encoder adaptations. As per equation 8,
the density of the power loss is Gaussian. However, our
optimization technique is applicable for any other distribution.
While optimizing 𝐿, we get the values of 𝑥2 , ⋅ ⋅ ⋅ , 𝑥𝑖 , ⋅ ⋅ ⋅ , 𝑥𝐿
using non-linear integer programming. To map the source
coding parameters, we can get encoding bitrate 𝜀𝑖 for range
between 𝑥𝑖 and 𝑥𝑖+1 as:(11)
𝜀𝑖 = 𝜀𝑖−1 + 𝜐𝑖 for 2 ≤ 𝑖 ≤ (𝐿 − 1)
where 𝜀1 = 𝜀𝑚𝑖𝑛 (𝑑), and 𝜀𝐿 = 𝜀𝑚𝑎𝑥 (𝑑).
B. Channel Adaptation
Channel selection by the secondary transmitter-receiver pairand subsequent transmissions will depend on the PU activities.
First, let us discuss PU activity and then present how SUs canavail the channels, not used by PUs.
1) PU Activity Model: We consider a 2-state ON-OFF
Markov model for the primaries. During the ON state, thesecondaries must remain inactive. However, during the OFF
state, the secondaries can choose to either remain idle ortransmit.
For the PUs, let the transition rate from OFF state to the ON
state be 𝛼 and transition rate from the ON state to the OFF
state be 𝛽. Then the probability of the PU being in the ON stateis given by: 𝑃𝑂𝑁 = 𝛼/(𝛼 + 𝛽). Similarly, the probability of
PU being in the OFF state is, 𝑃𝑂𝐹 𝐹 = 𝛽/(𝛼+𝛽). Here the ON
and OFF periods are assumed to be exponentially distributedwith means 1/𝛽 and 1/𝛼 respectively.
The cumulative distribution function (CDF) of the PU being
in the ON state for time 𝑡𝑜𝑛 is 𝐹𝑂𝑁 (𝑡) = 𝑃𝑂𝑁 (𝑡 ≤ 𝑡𝑜𝑛 ) =1 − 𝑒𝑥𝑝(−𝛽𝑡𝑜𝑛 ). Similarly, the CDF of the PU being in OFF
state for time 𝑡𝑜𝑓 𝑓 is 𝐹𝑂𝐹 𝐹 (𝑡) = 1 − 𝑒𝑥𝑝(−𝛼𝑡𝑜𝑓 𝑓 ).

Fig. 1. 3-state Markov Model for SU Activity2) SU Activity Model: Secondary users follow a 3 state
Markov model [17] as shown in Fig. 1. The three states are:
ON (SU transmitting), OFF (SU idle), and SS (Sensing State).
If we assume the arrival and departure rates of different statesaccording to Fig. 1, then the probability of SU being in the
ON state is 𝑃𝑆𝑂𝑁 = 𝜏 /(𝜏 + 𝜆); being in the OFF state is
𝑃𝑆𝑂𝐹 𝐹 = 𝜆/(𝜇 + 𝜆); and being in the SS state is 𝑃𝑆𝑆𝑆 =𝜇/(𝜇 + 𝜏 ). The CDF of the SU being in the ON state for time

𝑡𝑆𝑜𝑛 is 𝐹𝑆𝑂𝑁 (𝑡) = 1 − 𝑒𝑥𝑝(−𝜆𝑡𝑆𝑜𝑛 ). CDF of SU being in
OFF state for time 𝑡𝑆𝑜𝑓 𝑓 is 𝐹𝑆𝑂𝐹 𝐹 (𝑡) = 1 − 𝑒𝑥𝑝(−𝜇𝑡𝑆𝑜𝑓 𝑓 ).
Similarly, the CDF of the SU being in the SS state for time
𝑡𝑆𝑠𝑠 is 𝐹𝑆𝑆𝑆 (𝑡) = 1 − 𝑒𝑥𝑝(−𝜏 𝑡𝑆𝑠𝑠 ), assuming exponential
distribution as before.
Channel Selection( )Input : Set of PU Channels (𝑃 𝑈 ), Energy Threshold for
SU Channel (𝑇 ℎ𝑆𝑈 ), Threshold Increment (𝑇 ℎ𝑖𝑛𝑐 )Output: Selected PU Channel (𝑃 𝑈𝑆𝑒𝑙𝑒𝑐𝑡𝑒𝑑 )begin
𝑁 = No of PUs(𝑃 𝑈 );for 𝑖 ≤ 𝑁 do
𝐸[𝑖] = Energy (𝑃 𝑈 [𝑖]);𝑇 [𝑖] = PU availability (𝑃 𝑈 [𝑖]); /* From Sec.
IV-B1*/𝑆𝑡𝑜𝑟𝑒𝑇 [𝑖] = 𝑇 [𝑖]; /*Stores the initial values of 𝑇𝑖 */endlevel 1: while length(𝑇 ) > 0 do
𝑇 [𝑥] = maximum(T), ∀𝑖;/* Maximum PU
availability*/ if 𝐸[𝑥] ≤ 𝑇 ℎ𝑆𝑈 then𝑃 𝑈𝑆𝑒𝑙𝑒𝑐𝑡𝑒𝑑 = 𝑥;
break;endelse
𝑇 = 𝑇 - 𝑇 [𝑥];end
end
if 𝑃 𝑈𝑆𝑒𝑙𝑒𝑐𝑡𝑒𝑑 == NULL then𝑇 ℎ𝑆𝑈 = 𝑇 ℎ𝑆𝑈 + 𝑇 ℎ𝑖𝑛𝑐𝑟 ;for 𝑖 ≤ 𝑁 do
𝑇 [𝑖] = 𝑆𝑡𝑜𝑟𝑒𝑇 [𝑖] ;end
Goto level 1;
end
else
return 𝑃 𝑈𝑆𝑒𝑙𝑒𝑐𝑡𝑒𝑑 ;end
end

Algorithm 1: Algorithm for Spectrum Sensing andChannel Selection for Secondary Users
3) Spectrum Sensing and Channel Selection: There aremany spectrum sensing techniques like energy detection,
matched filter, cyclostationary [14] etc. However, energy detection is most popular because of its simplicity and robustness. As channel allocation is a NP Hard problem [12], we
present a heuristic threshold based energy detection mechanism for channel allocation to the SUs. For an SU, to use
a channel for a longer period of time, two conditions aredesired: (i) the energy level on that channel (or, any kind ofinterference) should be low and (ii) the PU should exhibit low
activity i.e., longer OFF periods.
We present the channel selection scheme by the SU in
Algorithm 1, where the SU selects a channel based on two
factors: (i) channel is available, and (ii) the detected energy
is lower than a threshold value. The first for loop stores
all the available PU channels. The PU channel with the
maximum availability is selected based on a threshold. Later,
the threshold is incremented if no PU channel is available andthe process is repeated.
V. I NDOOR T ESTBED I MPLEMENTATIONS
We have implemented the proposed adaptation techniques
on an indoor laboratory testbed. The overall block diagram
is shown in Figure 2. Though the setup allows for multiple
primary users and multiple secondary users, we implemented
one secondary transmitter-receiver pair, using two USRPs.
This could easily be extended for multiple SUs by considering
the channel occupied if any of the PUs or other SUs transmit
over that channel. Any smart channel allocation scheme canalso be applied for multiple SUs requesting the same channel
at same time, however, these issues are beyond the scope of
this paper. Spectrum sensing was done using a third USRP.
The SU scans a range of channels and maintains a list ofchannels that can be potentially used. First let us describe thedifferent blocks used for the testbed.
Video
SourceEncoder
H.264

Streamer
MPEG-TSStream to Vector
Converter
GMSK Modulation

USRP
Transmitter
AdjustingParamaters
SpectrumPU1 Sensing
Network

QoS

Gain
GNU Radio
Transmitter

PU2
PUn

Spectrum
Database

ChannelSelector

Analyzer
USRP
Receiver

Channel Filter
GMSK Demod

Display

Decoder
H.264

Connector

MPEG-TS
DEMUX
Feedback

Decoder
GNU Radio
ReceiverFig. 2. Blocks for Video Transmission using USRPs and GNU RadioVideo Source: The video source was a Logitech web-camwith a resolution of 720p. Real-time video was captured at 25
fps rate and streamed to the encoder for encoding.
Encoder: We used the MPEG-4 Part 10, also known as H.264,
to implement the Advanced Video Coding (AVC) standard.
H.264 is the most widely used video codec due to its efficient
implementation. We used x264 open source software libraries
for the implementation. We encoded the videos in 720p with
25 frames per second. Typically, once the encoding parameters
are set before the beginning of a video transmission, the rates
remain fixed for the entire duration of transmission. In our
implementation, these parameters are continuously updated
depending upon the feedback from the QoS Analyzer.
Streamer: The streamer uses UDP to stream the encoded
video over the network. The open source GStreamer [10]framework has been used for the experiments.
GNU Radio: We used GNU Radio [26] for implementing thetransmitter and receiver. The GNU Radio transmitter captures
the video frames from the streamer and processes them fortransmission over the RF channel, after signal processing.

GMSK modulation was used for improved spectral efficiency
and lower power consumption at the receiver [6].
USRPs: Universal Software Radio Peripherals (USRP) B210
with operational frequency range of 70 MHz to 6 GHz,
was used for our implementations. All USRPs have free
UHD driver support, open design schematic, and seamlessintegration features with the GNU Radio platform for signal
processing. VERT900 antennas (from Ettus Research) wereused to transmit and receive the processed signal. Both thetransmitting and receiving USRPs receive feedback from thechannel selector and set the RF parameters during runtime.
Mobile Transmitter and Lab Environment: The entire transmitter setup (web-cam, transmit antenna etc.) was mounted
on a remotely controlled mobile robotic platform as shown in
Fig. 3(a). Fig. 3(b) shows the lab environment. The indoor lab
environment exhibited considerable multipath effects due toconcrete walls. A Marvelmind indoor navigation system [27]working on 915 MHz ISM band was also present in theroom with 6 stationary and 4 mobile beacon elements, making
the RF environment congested. Along with all these, we had
multiple PU transmitter-receiver pairs during the experiments,
operating on the same ISM band. The receiver and the spectrum sensors were stationary as shown in Fig. 3(c).
Decoder and Display: At the receiver side, the H.264 decoder
was used to decode each frame considering 16×16 unit sizeand 9 prediction modes. The H.264 decoder had integrated
display of mplayer and GStreamer bin player.
Spectrum Sensing Network: One USRP was used for sensing
902 MHz to 926 MHz spectrum range with 1 second timeinterval for 1 MHz step size. For our experiments, we used
a USRP B210 with 70 dB receiver channel gain, placed
within 12 meters of both SUs. The sensed RSSI was fed
to a primitive database management station. We modified theusrp_spectrum_sense.py, a file provided by the uhd
driver installation.
Spectrum Database: We used a mysql database for storing
the spectrum sensing data. Though such a database was notessential, nevertheless we implemented it for the sake ofcompleteness for a larger distributed sensing framework. It
took input from the QoS analyzer as a feedback, and operated
on the data obtained from the sensing network.
Channel Selector: The channel selection tool was written in
Python. This tool fetched data from the database and sent to
the GNU Radio modules through TCP socket connections. We
implemented the threshold based channel selection techniqueas per Algorithm 1 for use with this tool. The sensing
network, mysql database, and channel selector worked in alive environment incurring about 1 second of overall delay.
QoS Analyzer: We used MSU video quality measurement
tool [28] for analyzing video QoS. When the QoS is below a
threshold at any time, a feedback is sent to: (i) the encoder
to set bitrate to a lower rate as per equation 11, and (ii) thechannel selector to inform the receiver and transmitter to hop
to the next best available channel as per Algorithm 1. Spectrum

Fig. 3. Experimental Scenario in Indoor Lab Environment

ValuesIndoor
Simplified Pathloss902-926 MHz (ISM Band)
3 MHz
GMSK
None
80 dB
70 dB
3 dBi
10 meter
1.7 [11]6.83 dB2 [11][-2.6134 to 2.6134] dB
512 Kbps
2048 Kbps
25 fps720p
MPEG-TSPSNR and SSIM
5 minutes

TABLE IC ONFIGURATION PARAMETERS FOR E XPERIMENTAL S ETUP

VI. E XPERIMENTAL R ESULTS
We present the experimental results for scenarios with:(i) no encoder adaptation and no channel adaptation, (ii)
encoder adaptation but no channel adaptation, (iii) no encoder
adaptation but channel adaptation, and (iv) both encoder andchannel adaptations. These four scenarios are presented in
sections VI-A to VI-D respectively. Due to the lack of existing
study for the same attributes considered here, we could notpresent a comparative study of proposed method with anyother implementation.
A. Video QoS without any Adaptation
In this set of experiments, we consider the channel tobe quasi-stable, i.e., the channel condition does not change
considerably. Here, we did not utilize the entire bandwidth

1

50

SSIM

PSNR
40

0.95

30

SSIM

Parameters
Experimental Scenario
Pathloss Model
Frequency
Bandwidth
Modulation Scheme
Error Correction Scheme
Transmitter Channel Gain
Receiver Channel Gain
Antenna Gain
Antenna Far Field Distance (𝑑0 )Pathloss Exponent (𝛾)Powerloss Variance (𝜎𝑠2 )Powerloss Deviation (𝜎𝑠 )Min Encoding Bitrate(𝜀𝑀 𝐼𝑁 )Max Encoding Bitrate(𝜀𝑀 𝐴𝑋 )Encoding Frame Rate
Video Resolution
Streaming Encapsulation
Video QoS
Each Experiment Run

by keeping encoding bitrate fixed at 512 Kbps. The frame
rate was set to 25 fps. The SU transmitter was mounted ona remotely operated mobile robot for all experiments. We
considered one secondary transmitter-receiver pair, where thereceiver was stationary. We controlled the distance of themobile transmitter such that it was at varying distances fromthe receiver. We observe from Fig. 4 that for the transmitterreceiver separation of 4 to 15 meters, the PSNR varied from
42 dB to 25 db. According to [23], PSNR between 30 dB to
50 dB, is considered to be good quality video. We achieved
PSNR of 34 dB at 12 meters. We also achieved SSIM of 0.99
to 0.961 for distances of 4 to 15 meters.

PSNR (dB)sensing, channel selection, and QoS analysis constituted thefeedback control mechanism for the overall implementation.
Configuration Parameters: The configuration parameters arepresented in Table I. All the experiments were conducted on
ISM bands (902 MHz to 926 MHz) with minimum sustaining bandwidth of 3 MHz. Higher bandwidth did not show
significant improvement of video quality. Usually antenna far
field reference distance (𝑑0 ) is 1 to 10 meters for indoor
environment. Pathloss exponent (𝜆) ranges between 1.6 and
1.8, inside buildings with Line Of Sight (LOS) [11]. During
our experiments we used 𝜎𝑠2 = 6.83 for 910 MHz [11].

0.9

20

0.85

10

0

4

6

8
10
Distance (meter)

12

14

0.8

4

6

8
10
Distance (meter)

12

14

(a)
(b)
Fig. 4. Video Quality with Encoding Rate= 512 Kbps (a) PSNR; (b) SSIM

B. Video QoS With Encoder Adaptation Only
Here we consider a continuously changing channel. We
simulated different types of channel diversity by number ofchanges in channel condition per minute. A “change” in thechannel condition occurs when the powerloss 𝜓(𝑥) goes fromone partition to another. We designed a software tool in C
using GTK+ libraries for this purpose. The proposed channel
adaptation technique was implemented to adapt to the varying
channel conditions and thereby provide best possible video
quality. The source coding bitrate was adapted based on the
distance and channel conditions. We considered minimum(𝜀𝑀 𝐼𝑁 ) and maximum (𝜀𝑀 𝐴𝑋 ) supported bitrates as 512
Kbps and 2048 Kbps respectively. From the previous set ofexperiments, we inferred that we can get good video quality
till 12 meters of separation between transmitter and receiver.
So we conducted this set of experiment for three distances:
4 meters, 8 meters, and 12 meters. We ran the experiments
for different number of channel adaptation rates (per minute).
From Fig. 5, we see PSNR ranges between 42 dB to 30 dB
for 0 to 60 changes per minute. The SSIM behaves like PSNR
for all distances. From Fig. 5 and Fig. 6, we infer that:

Video quality degrades as channel goes from slow to fast
fading.
∙ Good video quality is achieved until 40 channel conditionchanges per minute for a distance of 8 meters.
∙ Good video quality is achieved until 30 channel conditionchanges per minute for a distance of 12 meters.
∙

50
PSNR@4m
PSNR@8m
PSNR@12m

Channel adaptations provide better video quality.
Video quality degrades as the frequency of channel hopping increases.
∙ Good video quality is achieved with upto 3-5 hops per
minute in an indoor environment.
∙
∙

40

30

1 FH
3 FH
5 FH
7 FH
Non-DSA30PSNR (dB)

PSNR (dB)40

which is below the acceptable range of recognizable video
quality. In summary, from Figs. 7 and 8, we observe that:

20

10

20

10
0

0

10
20
30
40
50
No of Changes in Channel Condition (per min)60
0Fig. 5. PSNR for Continuously Changing Channel with Channel Adaptation

4

6

8
Distance (meter)

1012Fig. 7. PSNR for Fixed Channel Implementing Dynamic Spectrum Access
1.2
SSIM@4m
SSIM@8m
SSIM@12m

1.2
1 FH
3 FH
5 FH
7 FH
Non-DSA

1

0.8
SSIM

SSIM

1

0.8

0.6

0.6

0

10
20
30
40
50
No of Changes in Channel Condition (per min)

60

0.4
4

Fig. 6. SSIM for Continuously Changing Channel with Channel adaptation

C. Video QoS With Channel Adaptation Only
In this set of experiments, we implemented DSA considering multiple PUs, and one SU transmitter-receiver pair. We
consider 8 PU transmitters, all of which use a bandwidthof 3 MHz. The first PU uses 902 - 905 MHz, the second
PU uses 905 - 908 MHz, and so on. We started the SU
transmission at 910 MHz, and after sometime the primaryuser of that frequency was activated based on the PU activity
model. The SU transmitter pair continued to stream video byhopping to the next available frequency, which it obtained byconsulting the spectrum database. The channel selector used
the proposed threshold based energy detection algorithm toselect most suitable channel for the SU to operate. We ran
different sets of experiments considering that the SU needs todo frequency hopping (FH) for 1, 3, 5, 7 times per minute.
We also implemented a non-DSA scenario, where the SU pairstops to operate as soon as the PU gets active on that particularfrequency. Whenever the PU stopped its transmission, thesensor detects the PU absence and marks that channel as
usable. Fig. 7 shows that PSNR ranges from 32 - 29 dB forchannel hopping 1 to 7 times per minute at a distance of 4
meters. The non-DSA scenario provides worse video quality,

6

8
Distance (meter)

1012Fig. 8. SSIM for Fixed Channel Implementing Dynamic Spectrum Access

D. Video QoS With Both Encoder and Channel Adaptations
Here, we used the same set of experiments as we did for encoder adaptations (section VI-B) but with adapting the channel
5 times per minute. As expected, we observed improvements
over previous results. In Figs. 9 and 10, the solid lines
represent non-adaptive encoder (‘nAdap’) and the dashed lines
represent adaptive channels (‘Adap’). The behavior of PSNR
and SSIM changes are like the previous set of experiments.
We observe that the adaptive encoder provides a PSNR of
36.6 dB, whereas it was 32 dB for the non adaptive one. We
achieved enhancement in video quality metrics in each caseafter implementing the proposed encoder adaptation technique.
Finally, from Fig. 9 and 10, we infer that:
Joint adaptations of the encoder and channel provide
better video quality than the non-adaptive ones. The video
stalls at the SU receiver during PU interruption in nonDSA setting, resulting a degraded overall video quality.
∙ The trend of how the video quality changes is the sameas the previous set of experiments.
∙

40

PSNR (dB)30
1 FH-nAdap
1 FH-Adap
3 FH-nAdap
3 FH-Adap
5 FH-nAdap
5 FH-Adap
7 FH-nAdap
7 FH-Adap
Non-DSA

20

10

0

4

6

8
Distance (meter)

1012Fig. 9. PSNR for Changing Channel with Dynamic Channel Access
1.2

SSIM

1

1 FH-nAdap
1 FH-Adap
3 FH-nAdap
3 FH-Adap
5 FH-nAdap
5 FH-Adap
7 FH-nAdap
7 FH-Adap
Non-DSA

0.8

0.6

0.4
4

6

8
Distance (meter)

1012Fig. 10. SSIM for Changing Channel with Dynamic Channel AccessVII. C ONCLUSIONSThis paper presents a testbed implementation of dynamic
spectrum access for real-time video transmissions using SDRs
for multiple PUs and a single SU receiver-transmitter pair.
We propose a multi-level threshold based energy detection
technique for spectrum sensing and selection based on SU
and PU activity pattern. We have also presented a feedbackcontrolled channel adaptation technique targeted to enhancevideo quality for a continuously changing channel. We haveconcentrated on bitrate adaptation as source coding parameter,
but the proposed method can be applied to any parameterusing the same mechanism. We have shown that: (i) bettervideo quality is achieved by implementing DSA as opposed to
non-DSA based methods; (ii) the proposed channel adaptation
technique provides good quality video for a certain number ofchannel condition changes. Finally, we have implemented thefeedback-controlled channel adaptive mechanism for continuously changing channel using dynamic spectrum access. We
observe that channel adaptation enhances the video quality
for DSA implementation. Inclusion of live deployment ofspectrum database and more automated physical deployment
can be considered as a future direction of this work.
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