Abstract
We propose an analytically tractable approach to model compressed video traffic called C-DAR(1). The C-DAR(1) model combines an approach utilizing a discrete-time Markov chain with a continuous-time Markov chain. We show that this approach accurately models the distribution and exponential autocorrelation characteristics of video conferencing traffic. Also, we show that by comparing our analytical results against a simulation using actual video conferencing data, our model provides realistic results. In addition to presenting this new approach, we address the effects of long-range dependencies (LRD) in the video traffic. Based on our analytical and simulation results, we are able to conclude that the LRD have minimal impact on videoconference traffic modeling.
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