Low-Latency Network-Adaptive Error Control for Interactive Streaming

被引:3
|
作者
Emara, Salma [2 ]
Fong, Silas L. [1 ]
Li, Baochun [2 ]
Khisti, Ashish [2 ]
Tan, Wai-Tian [3 ]
Zhu, Xiaoqing [4 ]
Apostolopoulos, John [3 ]
机构
[1] Qualcomm Flar Technol, Bridgewater Township, NJ 08807 USA
[2] Univ Toronto, Dept Elect & Comp Engn, Toronto, ON M5S 3G4, Canada
[3] Cisco Syst, San Jose, CA 95134 USA
[4] Netflix Inc, 100 Winchester Cir, Los Gatos, CA 95032 USA
基金
加拿大自然科学与工程研究理事会;
关键词
Delays; Forward error correction; Streaming media; Decoding; Parity check codes; Internet; WebRTC; streaming codes; teleconferencing; CODES; VIDEO;
D O I
10.1109/TMM.2021.3070134
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We introduce a novel network-adaptive algorithm that is suitable for alleviating network packet losses for low-latency interactive communications between a source and a destination. Our network-adaptive algorithm estimates in real-time the best parameters of a recently proposed streaming code that uses forward error correction (FEC) to correct both arbitrary and burst losses, which cause a crackling noise and undesirable jitters, respectively in audio. In particular, the destination estimates appropriate coding parameters based on its observed packet loss pattern and sends them back to the source for updating the underlying code. Besides, a new explicit construction of practical low-latency streaming codes that achieve the optimal tradeoff between the capability of correcting arbitrary losses and the capability of correcting burst losses is used. Simulation evaluations based on statistical losses and real-world packet loss traces reveal the following: (i) Our proposed network-adaptive algorithm combined with our optimal streaming codes can achieve significantly higher performance compared to uncoded and non-adaptive FEC schemes over UDP (User Datagram Protocol); (ii) Our explicit streaming codes can significantly outperform traditional MDS (maximum-distance separable) streaming schemes when they are used along with our network-adaptive algorithm. In addition, we study different factors that can affect the performance of our network-adaptive algorithm.
引用
收藏
页码:1691 / 1706
页数:16
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