Heavy-Traffic Analysis of QoE Optimality for On-Demand Video Streams over Fading Channels

Ping-Chun Hsieh*, I. Hong Hou

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This paper proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems, where an access point transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON-OFF channels plus constant-bit-rate videos and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels and variable-bit-rate videos, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation and experimental results validate that the proposed policies indeed outperform existing policies.

Original languageEnglish
Article number8388739
Pages (from-to)1768-1781
Number of pages14
JournalIEEE/ACM Transactions on Networking
Volume26
Issue number4
DOIs
StatePublished - 1 Aug 2018

Keywords

  • Heavy traffic
  • quality of experience
  • scheduling
  • video streaming
  • wireless networks

Fingerprint Dive into the research topics of 'Heavy-Traffic Analysis of QoE Optimality for On-Demand Video Streams over Fading Channels'. Together they form a unique fingerprint.

  • Cite this