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Modeling MPEG Coded Video Traffic by Markov-Modulated Self-Similar Processes

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Abstract

Markov modulated self-similar processes are proposed to model MPEG video sequences that can capture the LRD (Long Range Dependency) characteristics of video ACF (Auto-Correlation Function). The basic idea is to decompose an MPEG compressed video sequence into three parts according to different motion/content complexity such that each part can individually be described by a self-similar process. Beta distribution is used to characterize the marginal cumulative distribution (CDF) of the self-similar processes. To model the whole data set, Markov chain is used to govern the transitions among these three self-similar processes. In addition to the analytical derivation, initial simulations have demonstrated that our new model can capture the LRD of ACF and the marginal CDF very well. Network cell loss rate using our proposed synthesized traffic is found to be comparable with that using empirical data as the source traffic.

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Authors and Affiliations

  1. Center for Communications and Signal Processing, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA

    Hai Liu, Nirwan Ansari & Yun Q. Shi

Authors
  1. Hai Liu
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  2. Nirwan Ansari
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  3. Yun Q. Shi
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Liu, H., Ansari, N. & Shi, Y.Q. Modeling MPEG Coded Video Traffic by Markov-Modulated Self-Similar Processes. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 29, 101–113 (2001). https://doi.org/10.1023/A:1011179732518

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