Recursive Non Linear Models for On Line Traffic Prediction of VBR MPEG Coded Video Sources

• Venue: IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
• Year: 2000

Any performance evaluation of broadband networks requires modeling of the actual network traffic. Since multimedia services and especially MPEG coded video streams are expected to be a major traffic component over these networks, modeling of such services and traffic prediction are useful for the reliable operation of the broadband based an Asynchronous Transfer Mode (ATM) networks. In this paper, a Recursive implementation of a Non linear AutoRegressive model (RNAR) is presented for on line traffic prediction of Variable Bit Rate (VBR) MPEG-2 video sources. This is accomplished by using an efficient weight adaptation algorithm so that the network provides good performance even in case of highly fluctuated traffic rates. In particular, the network weights are adapted so that the output is approximately equal to the current data while preserving the former knowledge of the network. Experimental results are presented to show the good performance of the proposed scheme. Furthermore, comparison with other linear or non-linear techniques is presented to show that the adopted method yields better results than the other ones.