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Optimizing hypervideo navigation using a Markov decision process approach

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Published:01 December 2002Publication History

ABSTRACT

Interaction with hypermedia documents is a required feature for new sophisticated yet flexible multimedia applications. This paper presents an innovative adaptive technique to stream hypervideo that takes into account user behaviour. The objective is to optimize hypervideo prefetching in order to reduce the latency caused by the network. This technique is based on a model provided by a Markov Decision Process approach. The problem is solved using two methods: classical stochastic dynamic programming algorithms and reinforcement learning. Experimental results under stochastic network conditions are very promising.

References

  1. MPEG-21, MPEG Group, Multimedia Framework, http://www.cselt.it/mpegGoogle ScholarGoogle Scholar
  2. B. Trousse, Evaluation of the Prediction Capability of a User behaviour Mining Approach for Adapative Web Sites, Proc. RIAO 2000, 6th Conference on Content-Based Multimedia Information Access, Paris, France, 2000.Google ScholarGoogle Scholar
  3. Apple, QuickTimeVR, http://quicktime.apple.comGoogle ScholarGoogle Scholar
  4. N. Sawhney, D. Balcom, I. Smith, Authoring and navigating video in space and time: A Framework and Approach towards Hypervideo, IEEE Multimedia, 4, 4, 1997 http://www.lcc.gatech.edu/gallery/hypercafe Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Tolva, MediaLoom, An Interactive Authoring Tool for HyperVideo, M.S. Project Paper, Georgia Institute of Technology, 1998.Google ScholarGoogle Scholar
  6. I. Zukerman, D. Albrecht, Predictive Statistical Models for User Modeling User Modeling and User-Adapted Interaction, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Crovella, P. Barford, The Network Effects of Prefetching, Proc. of Infocom'98, IEEE, April 1998.Google ScholarGoogle Scholar
  8. Y. Aumann et al., Predicting Event Sequences: Data Mining for Prefetching Web-pages, Proc. of KDD'98.Google ScholarGoogle Scholar
  9. K. Aberer, S. Hollfelder, Resource Prediction and Admission Control for Interactive Video Browsing Scenarios Using Application Semantics, GMD Report No. 40, September 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. D. Albrecht, I. Zukerman, A. Nicholson, Pre-sending Documents on the WWW: A comparative Study, Proc. IJCAI'99, August 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. T. Syeda-Mahmood, Learning and Tracking Browsing Behavior of Users using Hidden Markov Models, IBM Make It Easy Conference, 2001.Google ScholarGoogle Scholar
  12. T. Syeda-Mahmood, D. Ponceleon, Learning video browsing behavior and its application in the generation of video previews, Proc. ACM Multimedia 20001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Kohrs, B. Merialdo, Creating user-adapted Websites by the use of collaborative filtering, Interacting with Computers, Volume 13, Issue 6, August 2001.Google ScholarGoogle Scholar
  14. R. Grigoraş, V. Charvillat, M. Douze, Self-adaptive multimedia content, Proc. ECMCS, September 2001.Google ScholarGoogle Scholar
  15. R. E. Bellman, Dynamic programming, Princeton University Press, 1957. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. D. P. Bertsekas, J. N. Tsitsikis, Neurodynamic programming, Athena Scientific, Belmond, Massachusetts, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. M. L. Puterman, Markov decision processes: discrete stochastic dynamic programming, Wiley-Interscience, New York, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, Massachusetts, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Conferences
            MULTIMEDIA '02: Proceedings of the tenth ACM international conference on Multimedia
            December 2002
            683 pages
            ISBN:158113620X
            DOI:10.1145/641007

            Copyright © 2002 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 1 December 2002

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            MULTIMEDIA '02 Paper Acceptance Rate46of330submissions,14%Overall Acceptance Rate995of4,171submissions,24%

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