Prediction of MPEG-coded video source traffic using recurrent neural networks

Predicting traffic generated by multimedia sources is needed for effective dynamic bandwidth allocation and for multimedia quality-of-service (QoS) control strategies implemented at the network edges. The time-series representing frame or visual object plane (VOP) sizes of an MPEG-coded stream is extremely noisy, and it has very long-range time dependencies. This paper provides an approach for developing MPEG-coded real-time video traffic predictors for use in single-step (SS) and multistep (MS) prediction horizons. The designed SS predictor consists of one recurrent network for -VOPs and two feedforward networks for and -VOPs, respectively. These are used for single-frame-ahead prediction. A moving average of the frame or VOP sizes time-series is generated from the individual frame sizes and used for both SS and MS prediction. The resulting MS predictor is based on recurrent networks, and it is used to perform two-step-ahead and four-step-ahead prediction, corresponding to multistep prediction horizons of 1 and 2 s, respectively. All of the predictors are designed using a segment of a single MPEG-4 video stream, and they are tested for accuracy on complete video streams with a variety of quantization levels, coded with both MPEG-1 and MPEG-4. Comparisons with SS prediction results of MPEG-1 coded video traces from the recent literature are presented. No similar results are available for prediction of MPEG-4 coded video traces and for MS prediction. These are considered unique contributions of this research.