Using adaptive linear prediction to support real-time VBR video under RCBR network service model

A dynamic bandwidth allocation strategy to support variable bit rate (VBR) video traffic is proposed, This strategy predicts the bandwidth requirements for future frames using adaptive linear prediction that minimizes the mean square error, The adaptive technique does not require any prior knowledge of the traffic statistics nor assume stationarity. Analyses using six one-half-hour video traces indicate that prediction errors for the bandwidth required for the nest frames and Group of Pictures (GOP) are almost white noise or short memory. The performance of the strategy is studied using renegotiated constant bit rate (RCBR) network service model and methods that control the tradeoff between the number of renegotiations and network utilization are proposed, Simulation results using MPEG-I video traces for predicting GOP rates show that the queue size is reduced by a factor of 15-160 and the network utilization is increased between 190%-300% as compared to a fixed service rate. Results also show that even when renegotiations occur on the average in tens of seconds, the queue size is reduced by a factor between 16-30.