TRAFFIC MODEL FOR LAYERED VIDEO : AN APPROACH ON MARKOVIAN ARRIVAL PROCESS

Video transmission usually has stringent requirements on the network bandwidth, packet loss rate and the experienced delay. To better study the impact of layered video traffic on the network performance, accurate and tractable traffic model for layered video source is important. In this paper we propose a traffic model for scalable video encoded in multiple layers. The model is based on Markovian arrival process with marked transitions. The states of the Markovian arrival process are derived from the correlation feature found in the video data. The base layer and enhancement layer video frame size pairs are classified by cluster detection algorithm. Each cluster corresponds to one state of the underlying Markov chain of the video traffic arrival process. The joint base and enhancement layer video frame size distribution for each state of the Markov chain is approximated by multivariate normal distribution. Simulation study shows that the proposed traffic model can predict the network performance with high accuracy.

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