CWnd-Loan - A New Approach to Improve Live Video Performance in RTT-Spiking Networks
Refereed conference paper presented and published in conference proceedings
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AbstractWith the rapid advances in high-speed mobile networks such as 5G, Wi-Fi 6, and the upcoming 6G and Wi-Fi 7, streaming live video has become ubiquitous for mobile users. However, live video is susceptible to short-term network condition fluctuations which could lead to video stalls. Our investigations revealed that a substantial portion of such fluctuations were in fact caused by RTT spikes that were not congestion-related. These often confuse the transport protocol into dropping the transmission rate significantly, resulting in video stalls. This motivated us to develop a novel scheme called CWnd-loan to reduce the sender's CWnd-limited idle time during RTT spikes. We applied CWnd-loan to the QUIC protocol with BBR/CUBIC congestion control and strategically deployed it in a tier-1 live video service. The results show that CWnd-loan can effectively reduce sender CWnd-limited idle time by up to 18%, consequently reducing the duration and number of live video stalls by as much as 8.9% and 10.6%. Furthermore, CWnd-loan can also reduce the first-frame time and the playback failure rate by up to 3.2% and 2.7 % , respectively. CWnd-loan is designed to complement existing congestion control algorithms and thus could potentially be applied to current as well as future TCP/QUIC designs to tackle RTT spikes commonly found across mobile and wireless networks.
All Author(s) ListLingfeng Guo, Yan Liu, Jack Lee Yiu-Bun, Fuyu Wang, Changkui Ouyang, Cheng Luo, Wenzheng Yang, Shengtong Zhu, Kui Tan
Name of ConferenceIEEE International Conference on Network Protocols (ICNP)
Start Date of Conference10/10/2023
End Date of Conference13/10/2023
Place of ConferenceReykjavik
Country/Region of ConferenceIceland
Proceedings TitleProc. 2023 IEEE 31st International Conference on Network Protocols (ICNP)
Year2023
Month10
Day10
PublisherIEEE
eISBN979-8-3503-0322-3
eISSN2643-3303
LanguagesEnglish-United States
Keywordslive video, congestion control, RTT spikes, QoE, QoS, machine learning