Congestion Control for Web Real-Time Communication

Carlucci, Gaetano; Cicco, Luca De; Holmer, Stefan; Mascolo, Saverio

doi:10.1109/tnet.2017.2703615

Cited by 84 publications

(39 citation statements)

References 41 publications

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“…In this section we discuss the ML modeling results, implications and interpretations. Actually, the WebRTC statistics capture the joint impact of network impairments and the Google Congestion Control (GCC) algorithm included in the Google Chrome browser and implemented in the WebRTC project [20], [21]. The GCC algorithm is specifically designed to target real-time streams such as telephony and video conferencing [22], [23], [24], thereby trying to fully utilize the bottleneck link while keeping queuing delay small [24].…”

Section: Case Studymentioning

confidence: 99%

“…The GCC algorithm is specifically designed to target real-time streams such as telephony and video conferencing [22], [23], [24], thereby trying to fully utilize the bottleneck link while keeping queuing delay small [24]. According to [20], [21], the GCC algorithm implements both a delay-based and a loss-based controller, which is run on the sender side in response to feedback from the receiver. Considering the specific case of packet loss that we use in our case studies, we note that when a threshold of 10% packet loss is detected by the sender, GCC performs a new bandwidth estimate and subsequently invokes stream adaptation, including bitrate, resolution, and finally framerate adaptation.…”

Section: Case Studymentioning

confidence: 99%

“…Considering the specific case of packet loss that we use in our case studies, we note that when a threshold of 10% packet loss is detected by the sender, GCC performs a new bandwidth estimate and subsequently invokes stream adaptation, including bitrate, resolution, and finally framerate adaptation. The interested reader is referred to [20], [21] and [24] for further details regarding GCC. In the sequel, we will see the impacts of network impairments, GCC algorithm and user perceivable quality metrics (such as video blockiness and audio distortion) in the ML models.…”

Section: Case Studymentioning

confidence: 99%

See 2 more Smart Citations

Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

Ammar

Moor

Skorin‐Kapov

et al. 2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

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We address the challenge faced by service providers in monitoring Quality of Experience (QoE) related metrics for WebRTC-based audiovisual communication services. By extracting features from various application-layer performance statistics, we explore the potential of using machine learning (ML) models to estimate perceivable quality impairments and to identify root causes. We argue that such performance-related data can be valuable and informative from a QoE assessment point of view, by allowing to identify the party/parties in a call that is/are experiencing quality impairments, and to trace the origins and causes of the problem. The paper includes case studies of multi-party videoconferencing that are established in a laboratory environment and exposed to various network disturbances and CPU limitations. Our results show that perceivable quality impairments in terms of video blockiness and audio distortions may be estimated with a high level of accuracy, thus proving the potential of exploiting ML models for automated QoE-driven monitoring and estimation of WebRTC performance.

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Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

Ammar

Moor

Skorin‐Kapov

et al. 2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

View full text Add to dashboard Cite

show abstract

“…The researchers found that the threshold, γ, has a significant impact on the dynamics of the receiving rate Ar, channel utilization, queuing delay, loss ratio, and the fairness of the WebRTC flow when coexisting with a TCP flow. Therefore, authors in [9] have suggested not to use the default value of the threshold, which is γ = 25/60 ms. As a remedy, authors in [10] proposed a mathematical model for adapting γ dynamically to provide fair coexistence of WebRTC flows with TCP flows.…”

Section: Google Congestion Control Algorithmmentioning

confidence: 99%

A Dynamic Approach to Estimate Receiving Bandwidth for WebRTC

Iqbal

Shirmohammadi

Atwah

2016

International Journal of Multimedia Data Engineering and Management

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Web Real-Time Communication (WebRTC), drafted by the World Wide Web Consortium (W3C) and Internet Engineering Task Force (IETF), enables direct browser-to-browser real-time communication. As its congestion control mechanism, WebRTC uses the Google Congestion Control (GCC) algorithm. But using GCC will limit WebRTC's performance in cases of overusing due to using a fixed decreasing factor, known as alpha (a). In this paper, the authors propose a dynamic alpha model to reduce the receiving bandwidth estimate during overuse as indicated by the overuse detector. Using their proposed model, the receiver can more efficiently estimate its receiving rate in case of overuse. They implemented their model over both unconstrained and constrained networks. Experimental results show noticeable improvements in terms of higher incoming rate, lower Round-Trip Time, and lower packet loss compared to the fixed alpha model.

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“…As the rise of video telephony service, it is necessary to deploy rate control algorithms to avoid congestion and to promote fair bandwidth allocation. The quality of video telephony service is not only affected by goodput but also by latency, which should be kept as low as possible 6 . Traditional congestion control algorithms in TCP are designed to optimize throughput and no attempt is made to minimize delay.…”

Section: Introductionmentioning

confidence: 99%

An online learning based path selection for multipath real‐time video transmission in overlay network

Zhang

Lei

Zhang

et al. 2020

Trans Emerging Tel Tech

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Even video telephony services have been pervasively applied, it is still a challenge to provide satisfactory quality of experience to users. Multipath transmission is a promising solution to improve video transmission quality by providing aggregated bandwidth. Due to varied traffic load, the default paths may fall into long lasting congestion. Sender has no choice but to reduce video bitrate to adapt such change, even in multipath transmission scenario. Insisting previous rate may lead the congestion in bottleneck deteriorate further. Deploying relay servers in current Internet infrastructure provides users with alternatives to bypass path failure and congestion. For such transmission scenario for video telephony service, an arising question is how to select paths dynamically to gain maximum profit. The path selection is mapped to the multi‐armed bandit problem. Tradeoff is made between exploration and exploitation to make decision under uncertain environment. The estimated bandwidth provided by congestion controller is used for decision‐making. Further, to make multiple flows share resource fairly and avoid congestion, an algorithm adapted from BBR is implemented for video telephony service. To maintain throughput stability and fairness, a smaller probe up gain value is used and the cycle length in bandwidth probe phase is randomized. To reduce delay, the inflight packets will be drained to match with bandwidth delay product in the probe down phase. The effectiveness of the proposed solution is verify on simulation platform.

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Congestion Control for Web Real-Time Communication

Cited by 84 publications

References 41 publications

Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

A Dynamic Approach to Estimate Receiving Bandwidth for WebRTC

An online learning based path selection for multipath real‐time video transmission in overlay network

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