Physical-Layer Informed Multipath Redundancy
Optimization for Mobile Real-Time Communication
Jing Chen1,3 , Zili Meng1,3 , Mingwei Xu1,2,3
1 Institute for Network Sciences and Cyberspace, 2 Department of Computer Science and Technology, Tsinghuabuffer University

3 Beijing National Research Center for Information Science and Technology (BNRist)

j-chen16@tsinghua.org.cn,zilim@ieee.org,xumw@tsinghua.edu.cn

CCS CONCEPTS• Networks → Mobile networks; Cross-layer protocols.
ACM Reference Format:
Jing Chen, Zili Meng, Mingwei Xu. 2021. Physical-Layer Informed
Multipath Redundancy Optimization for Mobile Real-Time Communication. In 5th Asia-Pacific Workshop on Networking (APNet 2021)(APNet ’21), June 24–25, 2021, Shenzhen, China. ACM, New York,NY, USA, 2 pages. https://doi.org/10.1145/3469393.3469673

1

INTRODUCTION

Mobile real-time communication (RTC) applications (e.g.,video conferencing, cloud gaming) are growing popular, butpose strict (i.e., millisecond-level) requirements on the endto-end user-perceived latency. Various mechanisms at thetransport layer [1] are proposed to reduce the end-to-end
latency by carefully designing mechanisms against congestion and packet losses. Specially, for mobile scenarios where
the capacity of the last-mile link frequently experiences highvariations [5], merely relying on transport layer adaption
cannot achieve satisfactory quality of experience. Therefore,
a common way to further reduce the tail latency is to duplicatively send data over several independent cellular connections on user devices [2, 3], which offers a much greater
chance to deliver the traffic in time at the application layer.
To accurately mask the tail latencies with minimum additional bandwidth, it is crucial to decide the proper multipathredundancy rate. However, existing multipath redundancy
solutions fail to strike a good balance between latency andgoodput for mobile RTC. On one hand, aggressively duplicating traffic on multipaths oblivious to the path conditions (e.g.,
Supported by National Key R&D Program of China (2018YFB1800302) andNSFC (61625203 and 92038302). Mingwei Xu is the corresponding author.
Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are notmade or distributed for profit or commercial advantage and that copies bear
this notice and the full citation on the first page. Copyrights for componentsof this work owned by others than ACM must be honored. Abstracting withcredit is permitted. To copy otherwise, or republish, to post on servers or toredistribute to lists, requires prior specific permission and/or a fee. Request
permissions from permissions@acm.org.
APNet ’21, June 24–25, 2021, Shenzhen, China© 2021 Association for Computing Machinery.
ACM ISBN 978-1-4503-8587-9/21/06. . . $15.00
https://doi.org/10.1145/3469393.3469673

𝛼1Transport
Scheduler

data

Cell Tower

Profiler

UESINR ↓PhysicalEPC
𝛼2quality ↓dataRTC Server

Figure 1: PhyRO Overview. The UE calculates redundancy rates
α 1 ,α 2 based on PHY-layer indicators and requests the server toschedule redundant data accordingly.

ReMP TCP [2]) could compellingly reduce the tail latency, butwould introduce unnecessary redundant transmission andconsume more bandwidth. On the other hand, optimizations
towards high goodput could save bandwidth and increase
the goodput yet have little improvement in terms of latency.
The failure to balance between latency and goodput rootsin the delayed observation of path degradation at the transport layer. When reactively duplicating traffic after observing
the degradation of one path, the packet delivery time to the
RTC application would be at least delayed for one RTT of theslow path. Therefore, our key observation is that physicallayer indicators (e.g., signal strength) can more timely reflect
the degradation of cellular channels with 0.5 RTT in advanceof transport-layer indicators. As illustrated in Fig. 1, when
the channel quality of the cellular last mile worsens, the user
equipment (UE) almost immediately observes a drop in thesignal strength. However, the transport layer can only observe the degradation by receiving a successfully delivered
packet from the cell tower and update the RTT information.
Considering the last mile is becoming the major bottleneckof RTC services, such a time (the packet delivery time fromthe cell tower to the UE) could be as high as almost 0.5 RTT,
which severely increases the delay at the application layer.
In response, we propose PhyRO, a multipath redundancy
optimizer that takes PHY-layer information as the criterion.
The design challenges are (1) which PHY-layer indicator
shall we use, (2) how to use it, and (3) how to balance between latency and goodput in choosing the path and thecorresponding redundancy rates. In response, we choose thesignal strength as the indicator based on its close relation
to the channel quality. PhyRO uses a Profiler to predictthe probability distribution of the transport-layer latency
based on real-time signal strengths. Then, PhyRO introducesa Scheduler to optimize the redundancy rate by mathematically seeking a balance between tail latency and goodput

APNet ’21, June 24–25, 2021, Shenzhen, ChinaIII

II: 𝐹𝐷1 𝜏 ≥ 1 − 𝑝𝑡ℎ
VI: 𝐹𝐷2 𝜏 ≥ 1 − 𝑝𝑡ℎState 1: PATH1

VIIV: 𝐹𝐷2 𝜏 < 1 − 𝑝𝑡ℎ

0 .8Figure 2: The Scheduling FSM

2DESIGN

Multiple PHY-layer indicators are used in cross-layer networking designs, including the downlink physical resource
block (PRB) allocation and signal strength related indicators(e.g., RSSI, RSRP, SINR) [5]. Since the available bandwidthreflects the combined influence of the path condition andcompeting traffic, we focus on the signal strength, specifically, the signal to interference noise ratio (SINR), which
directly reflects the path condition and is accessible to theupper layers via multiple tools (e.g., MobileInsight [4]).
To tradeoff between the bandwidth consumption and latency and choose proper redundancy rates, we design 2
components (shown in Fig. 1): the Profiler first uses themeasured signal strength to profile the delay distribution,and then the Scheduler optimizes the redundancy decisions
with a finite state machine (FSM) and probability modelling.
We first introduce some denotations. Let D represent theone-way delay (OWD) and s represent the SINR value. We
denote the maximum tolerable user-perceived one-way delay
as τ and the maximum tolerable stuttering rate as pth . The
decision is expressed as α 1 and α 2 , which are the redundancy
rates for data to be transferred on path1 and path2. We denote
the Cumulative Distribution Functions (CDF) of the OWD
on the two paths as F D 1 (·) and F D 2 (·).
The Profiler aims at estimating the probability distribution of OWD given a SINR measurement, i.e, F D (·|s). As apreliminary attempt, we collect SINR and OWD traces and fit
the OWD values into a Gaussion distribution given a SINR.
The Scheduler maintains a FSM with 3 states: concurrently using the 2 paths (DUAL-PATH), only using the first path(PATH1) and only using the second path (PATH2), as shown inFig. 2. When the Profiler updates the OWD distributions
based on a new SINR measurement, the Scheduler checks
the triggering conditions (marked in Fig. 2), goes to the next
state and calculates (α 1 , α 2 ) as follows:
For transition II, III, VI and VII where one path is enoughto keep the stuttering rate lower than pt h , data are scheduled
only to that path (e.g., α 1 = 1, α 2 = 0 for II).
For the state transition I (and similarly, V) where the previously used path is not enough, we should immediately
schedule redundant data on the other path, ensuring thatthe overdue probability is smaller than pt h :[1 − F D 1 (α 1τ )][1 − F D 2 (α 2τ )] ≤ pt h(1)0 .90 .8 5

State 3: PATH2

with a finite state machine. Evaluation with NS-3 shows that
PhyRO improves the 95t h percentile stuttering rate by 54.8%
against LowRTT scheduler, with negligible goodput loss.

1000 .0 10 .0 20 .0 3

P h y R O
R e M P
L o w R T T0 .0 4(a) Stuttering Rate

C D F

I: 𝐹𝐷1 𝜏 < 1 − 𝑝𝑡ℎ

10 .9 5

State 2: DUAL-PATHC D F

VIJ. Chen et al.

0 .0 50 .80 .60 .40 .200 .7 2 0 .7 4 0 .7 60 .9 6 0 .9 81(b) Goodput Ratio

Figure 3: Simulation results on 69 7-minute traces.

So the redundant scheduling decision should be:
α 1 = 1, α 2 = F D−12 [1 − pth /(1 − F D 1 (τ ))]/τ(2)
For the state transition VI, we adaptively renew the redundancy rates on dual paths by minimizing the bandwidthconsumption (α 1 + α 2 ) under the constraint of Eqn. 1. We
optimize with the Lagrange Multiplier method, getting:α 1 /(α 1 +α 2 )α 2 /(α 1 +α 2 )F D 1 (α 1τ ) = 1 − pth, F D 2 (α 2τ ) = 1 − pth(3)However, solving α 1 , α 2 from Eqn. 3 is nontrivial, since
it is a set of transcendental equations with α 1 and α 2 coupling together. In response, we adopt the Coordinate Descentmethod to iterate α 1 and α 2 in turns until their convergence.

3EVALUATION & FUTURE WORK

Our preliminary simulations are based on NS-3 traces. We
simulate the wireless link propagation loss with an ITU-R1411 NLoS model (for Non-Line-of-Sight short-range outdoor
communication in the frequency range 300 MHz to 100 GHz).
We simulate UE mobility with a random walking model. For
comparison, we use the a ReMP scheduler (which duplicates
all traffic on all available paths) and a LowRTT scheduler(which selects the path with the lowest RTT) as baselines. In
the experiment, we set τ = 100ms and pth = 0.0001.
We present the CDF of the stuttering rate (defined as theproportion of packets that miss the latency requirement)on 69 traces in Fig. 3(a). PhyRO reduces the 95th -percentile
stuttering rate by 54.8% against the LowRTT scheduler, andachieves relatively close performance to the optimal stuttering rate of ReMP scheduler. We also measure the goodputratio (goodput / total throughput) , and the goodput ratios
of ReMP and LowRTT are constantly 0.5 and 1 by their design.
As shown in Fig. 3(b), PhyRO has a very high goodput ratio(>0.995 by 92.7%), i.e., PhyRO reduces the stuttering rate ofLowRTT with negligible bandwidth overhead.
In the future, we will take more practical factors (e.g.,energy overhead, the impact of various cellular standards)
into consideration and conduct testbed experiments.

REFERENCES[1] Gaetano Carlucci, Luca De Cicco, et al. 2017. Congestion control for
web real-time communication. IEEE/ACM Trans. Netw. (2017).
[2] Alexander Frommgen, Tobias Erbshäußer, Alejandro Buchmann, et al.
2016. ReMP TCP: Low latency multipath TCP. In Proc. IEEE ICC.
[3] HyunJong Lee, Jason Flinn, et al. 2018. RAVEN: Improving interactive
latency for the connected car. In Proc. ACM MobiCom.
[4] Yuanjie Li et al. 2016. Mobileinsight: Extracting and analyzing cellularnetwork information on smartphones. In Proc. ACM MobiCom.
[5] Yaxiong Xie et al. 2020. PBE-CC: Congestion Control via EndpointCentric, Physical-Layer Bandwidth Measurements. In ACM SIGCOMM.