Towards Optimal Low-Latency Live Video Streaming

Liyang Sun, Tongyu Zong, Siquan Wang, Yong Liu, Yao Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It poses significant challenges for streaming over the Internet. In this paper, we explore the design space of low-latency live streaming by developing dynamic models and optimal adaptation strategies to establish QoE upper bounds as a function of the allowable end-to-end latency. We further develop practical live streaming algorithms within the iterative Linear Quadratic Regulator (iLQR) based Model Predictive Control and Deep Reinforcement Learning frameworks, namely MPC-Live and DRL-Live, to maximize user live streaming QoE by adapting the video bitrate while maintaining low end-to-end video latency in dynamic network environment. Through extensive experiments driven by real network traces, we demonstrate that our live streaming algorithms can achieve close-to-optimal performance within the latency range of two to five seconds.

Original languageEnglish (US)
Pages (from-to)2327-2338
Number of pages12
JournalIEEE/ACM Transactions on Networking
Volume29
Issue number5
DOIs
StatePublished - Oct 1 2021

Keywords

  • iterative linear quadratic regulator
  • Live streaming
  • reinforcement learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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