A Long Horizon Neuro-fuzzy Predictor for MPEG Video Traffic

This paper investigates the long-term prediction of MPEG video traffic. Predicting such traffic over a long horizon is important for today’s fast networks and internet multimedia services. In comparison with short-term prediction, long-term prediction of video traffic is yet to be explored especially for MPEG-4 coded videos despite its effectiveness in a number of important network-edge applications such as dynamic bandwidth allocation, quality of service (QoS) control, and network management and planning. The main reason for the shortage of publications in such area is the difficulty of the problem, especially when classical or widely used prediction techniques are the ones to be employed. Prediction results, in this paper, are obtained using a simple neuro-fuzzy system and are compared to the classical normalized Least Mean Squares (LMS) technique. The neuro-fuzzy predictor is capable of predicting various real MPEG-4 real-world video traffic hundreds of frames in advance with high accuracy.