Congestion control using FEC for conversational multimedia communication

Nagy, Marcin; Singh, Varun; Ott, Jörg; Eggert, Lars

doi:10.1145/2557642.2557649

Cited by 19 publications

(6 citation statements)

References 30 publications

(48 reference statements)

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“…WebRTC congestion control: SCReAM [17] is a hybrid loss and delay based congestion control algorithm for conversational video over LTE. FBRA [19] proposes a FEC-based congestion control algorithm that probes for the available bandwidth through FEC packets. In the case of losses due to congestion, the redundant packets help in recovering the lost packets.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of WebRTC-based Video Conferencing

Jansen

Goodwin

Gupta

et al. 2018

SIGMETRICS Perform. Eval. Rev.

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WebRTC has quickly become popular as a video conferencing platform, partly due to the fact that many browsers support it. WebRTC utilizes the Google Congestion Control (GCC) algorithm to provide congestion control for realtime communications over UDP. The performance during a WebRTC call may be influenced by several factors, including the underlying WebRTC implementation, the device and network characteristics, and the network topology. In this paper, we perform a thorough performance evaluation of WebRTC both in emulated synthetic network conditions as well as in real wired and wireless networks. Our evaluation shows that WebRTC streams have a slightly higher priority than TCP flows when competing with cross traffic. In general, while in several of the considered scenarios WebRTC performed as expected, we observed important cases where there is room for improvement. These include the wireless domain and the newly added support for the video codecs VP9 and H.264 that does not perform as expected.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of WebRTC-based Video Conferencing

Jansen

Goodwin

Gupta

et al. 2018

SIGMETRICS Perform. Eval. Rev.

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show abstract

“…Many congestion control algorithms have been proposed for peer-to-peer conversational real-time video, which attempt to match the media rate to the available end-to-end path capacity. These algorithms [18,15,19,20,11,9,12,23,6] rely on the congestion indicators reported in the RTCP Receiver Reports from the remote endpoint.…”

Section: Related Workmentioning

confidence: 99%

Last N

Grozev

Marinov

Singh

et al. 2015

Proceedings of the 25th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video

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Multiparty conferencing has traditionally been a relatively expensive application that was only used in enterprise scenarios. Recently, however, the landscape has started to shift in ways that could change this. Ever-increasing bandwidth and processing capabilities make it possible for mobile endpoints and laptop computers to easily handle multiple incoming media streams (both audio and video). The development of Web Real-Time Communications (WebRTC) has also significantly simplified the development of video conferencing applications and made them mainstream. Both of these changes provide a way of replacing expensive video mixers (that produce composited videos) with light-weight video routers (that selectively forward streams). In this paper, we describe a Multipoint Control Unit (MCU) that identifies and selects the last N dominant speakers and forwards their streams to all the conference participants. We evaluate the performance of this Selective Forwarding Unit (SFU) against a simplistic everyone-to-everyone (full-star) MCU. Our results show that the SFU uses 45% less CPU and 63% less bandwidth when forwarding media for 10 of the endpoints in a 30-participant conference.

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“…In the case of interactive communications like voice and video calls, the service provider (or the application) must select the data rate used to encode the voice and video signals at the beginning, and then this rate changes slowly [4].…”

Section: Introductionmentioning

confidence: 99%

Predicting file downloading time in cellular network: Large-Scale analysis of machine learning approaches

et al. 2018

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Downlink data rates can vary significantly in cellular networks, with a potentially non-negligible effect on the user experience. Content providers address this problem by using different representations (e.g., picture resolution, video resolution and rate) of the same content and switch among these based on measurements collected during the connection. Knowing the achievable data rate before the connection establishment should definitely help content providers to choose the most appropriate representation from the very beginning. We have conducted several large measurement campaigns involving a panel of users connected to a production network in France, to determine whether it is possible to predict the achievable data rate using measurements collected, before establishing the connection to the content provider, on the operator's network and on the mobile node. We establish evidence that it is indeed possible to exploit these measurements to predict, with an acceptable accuracy, the achievable data rate.We thus introduce cooperation strategies between the mobile user, the network operator and the content provider to implement such anticipatory solution.

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Congestion control using FEC for conversational multimedia communication

Cited by 19 publications

References 30 publications

Performance Evaluation of WebRTC-based Video Conferencing

Performance Evaluation of WebRTC-based Video Conferencing

Last N

Predicting file downloading time in cellular network: Large-Scale analysis of machine learning approaches

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