High-QoE DASH Live Streaming Using Reinforcement Learning

Bo Wei, Hang Song, Jiro Katto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the live video streaming becomes more and more common in daily life such as live meeting and live video call, it is an urgent task to ensure high-quality and low-delay live video streaming service. High user quality of experience (QoE) should be ensured to satisfy the requirement of user, for which latency is one of the important factors. In this paper, a high-QoE live streaming method is proposed with reinforcement learning. Experiments are conducted to evaluate the proposed method. Results demonstrate that the proposal shows the best performance with highest QoE compared with conventional methods in three network conditions. In Ferry case, the QoE is almost twice of the QoE of other methods.

Original languageEnglish
Title of host publication2021 IEEE/ACM 29th International Symposium on Quality of Service, IWQOS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665414944
DOIs
Publication statusPublished - 2021 Jun 25
Event29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021 - Virtual, Tokyo, Japan
Duration: 2021 Jun 252021 Jun 28

Publication series

Name2021 IEEE/ACM 29th International Symposium on Quality of Service, IWQOS 2021

Conference

Conference29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021
Country/TerritoryJapan
CityVirtual, Tokyo
Period21/6/2521/6/28

Keywords

  • DASH
  • live streaming
  • QoE
  • reinforcement learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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