PREDICTIVE DYNAMIC BANDWIDTH ALLOCATION FOR EFFICIENT TRANSPORT OF REAL-TIME VBR VIDEO OVER ATM

This paper presents a novel approach to dynamic transmission bandwidth allocation for transport of real-time variable-bit-rate video in ATM networks. Video traffic statistics are measured in the frequency domain: The low-frequency signal captures the slow time variation of consecutive scene changes while the high-frequency signal exhibits the feature of strong frame autocorrelation. Our queueing study indicates that the video transmission bandwidth in a finite-buffer system is essentially characterized by the low-frequency signal. We further observe in typical JPEG/MPEG video sequences that the time scale of video scene changes is in the range of a second or longer, which localizes the low-frequency video signal in a well-defined low-frequency band. Hence, in a network design it is feasible to implement dynamic allocation of video transmission bandwidth using on-line observation and prediction of scene changes. Two prediction schemes are examined: recursive least square method and time delay neural network method. A time delay neural network with low-complexity high-order architecture, called ''pi-sigma network,'' is successfully used to predict scene changes. The overall dynamic bandwidth-allocation scheme presented in this paper is shown to be promising and practically feasible in obtaining efficient transmission of real-time video traffic.