Source models for VBR broadcast-video traffic

Traffic from video services is expected to be a substantial portion of the traffic carried by emerging broadband integrated networks. For variable bit rate (VBR) coded video, statistical source models are needed to design networks that achieve acceptable picture quality at minimum cost, and to design traffic shaping and control mechanisms. For video teleconference traffic, the authors have previously shown that traffic is sufficiently accurately characterized by a multi-state Markov chain model that can be derived from three traffic parameters (mean, correlation, and variance). In the present paper, they describe modeling results for sequences with frequent scene changes (the previously studied video teleconferences have very little scene variation) such as entertainment television, news, and sports broadcasts. They analyze eleven long sequences of broadcast video traffic data. Unlike video teleconferences, the different sequences studied have different details regarding distributions of cells per frame. The authors present source models applicable to the different sequences and evaluate their accuracy as predictors of cell losses in asynchronous transfer mode (ATM) networks