SDNHAS: An SDN-enabled architecture to optimize QoE in HTTP adaptive streaming

Abdelhak Bentaleb, Ali C. Begen, Roger Zimmermann, Saad Harous

Research output: Contribution to journalArticlepeer-review

80 Citations (Scopus)

Abstract

HTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture called SDNDASH [1] to address HAS scalability issues including video instability, quality of experience (QoE) unfairness, and network resource underutilization, while maximizing per player QoE. While SDNDASH was a significant step forward, there were three unresolved limitations: 1) it did not scale well when the number of HAS players increased; 2) it generated communication overhead; and 3) it did not address client heterogeneity. These limitations could result in suboptimal decisions that led to viewer dissatisfaction. To that effect, we propose an enhanced intelligent streaming architecture, called SDNHAS, which leverages software defined networking (SDN) capabilities of assisting HAS players in making better adaptation decisions. This architecture accommodates large-scale deployments through a cluster-based mechanism, reduces communication overhead between the HAS players and SDN core, and allocates the network resources effectively in the presence of short- and long-term changes in the network.

Original languageEnglish
Article number7995089
Pages (from-to)2136-2151
Number of pages16
JournalIEEE Transactions on Multimedia
Volume19
Issue number10
DOIs
Publication statusPublished - Oct 2017

Keywords

  • Bitrate adaptation logic
  • Convex optimization
  • DASH
  • FastMPC
  • HTTP adaptive streaming (HAS)
  • Instability
  • OpenFlow
  • Quality of experience (QoE)
  • Reinforcement learning
  • Scalability
  • Software defined networking (SDN)
  • Streaming architecture
  • Underutilization
  • Unfairness

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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