Distributed prefetching scheme for random seek support in peer-to-peer streaming applications

  • Authors:

  • Changxi Zheng;Guobin Shen;Shipeng Li

  • Affiliations:

  • Shanghai Jiaotong University, Shanghai, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the ACM workshop on Advances in peer-to-peer multimedia streaming
  • Year:
  • 2005

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Abstract

Through analysis of large volume of user behavior logs during playing multimedia streaming, we extract a user viewing pattern. The pattern indicates that random seek is a pervasive phenomenon, contrary to the common assumptions that users would watch a video session sequentially and passively in most works on peer-to-peer streaming. We propose to use efficient prefetching to facilitate the random seek functionality. Because of the statistical nature of the user viewing pattern and the ignorance of the users to the content, we argue that the pattern should be used as a guidance to the random seek. Based on the pattern, we set up an analogy between the optimization problem of minimizing the seeking distance and the optimal scalar quantization problem. We then propose an optimal prefetching scheduling algorithm based on the optimal scalar quantization theory. We further propose a hierarchical prefetching scheme to carry out the prefetching more effectively. Real user viewing logs are used to drive the simulations which demonstrate that the proposed prefetching scheduling algorithm and the hierarchical prefetching scheme can improve the seeking performance significantly.