Waiting-time prediction in scalable on-demand video streaming

  • Authors:

  • Nabil J. Sarhan;Mohammad A. Alsmirat;Musab Al-HADRUSI

  • Affiliations:

  • Wayne State University, Detroit, MI;Wayne State University, Detroit, MI;Wayne State University, Detroit, MI

  • Venue:
  • ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
  • Year:
  • 2010

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Abstract

Providing video streaming users with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait. In the absence of any waiting-time feedback, users are more likely to defect because of the uncertainty as to when their services will start. We analyze waiting-time predictability in scalable video streaming. We propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits.