Workload-aware resource sharing and cache management for scalable video streaming

  • Authors:

  • Bashar Qudah;Nabil J. Sarhan

  • Affiliations:

  • Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI;Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI

  • Venue:
  • IEEE Transactions on Circuits and Systems for Video Technology
  • Year:
  • 2009

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Abstract

The required real-time and high-rate transfers for multimedia data severely limit the number of video streams that can be delivered concurrently. Resource-sharing techniques address this problem and can be classified into two main classes: stream merging and periodic broadcasting. We evaluate through extensive simulation major resource-sharing techniques from the two classes, considering different service models and video workloads. We utilize this extensive analysis in developing a workload-aware hybrid solution (WAHS) that combines the advantages of the best performers among resource-sharing techniques. Moreover, we propose a statistical cache management (SCM) approach and derive analytical models for optimal cache allocation to reduce further the demands on the disk I/O when various resource sharing techniques are used.