面向RCBR网络的变比特率MPEG视频流的自适应预测

变比特率视频业务是将来多媒体业务的主要组成部分，为保证高质量，实时传输的需要，准确把握视频特征，结合网络传输机制为多媒体通信提供更好的服务质量（QoS)是目前亟待解决的问题之一，该文针对面向RCBR（Renegotiated Constant Bit Rate)网络的变比特率MPEG视频提出了基于编码结构的自适应预测算法，它充分利用了视频流的周期相关性，能准确快速，无延迟地对视频帧的码率进行预测，通过网络动态地为视频分配合适的带宽，不仅能达到高质量，低延迟抖动的视频服务，并且提高了网络的利用率，与分配固定带宽的网络相比，其网络缓冲的排除性能和网络的利用率有明显提高。

Adaptive Prediction of Variable Bit Rate MPEG Video Traffic for RCBR Network

Recently researchers have brought forward some models to describe the characteristics of video traffic. For instance, neural network, non-adaptive time-domain linear filtering, adaptive time-domain linear filtering, and wavelet-domain methods for video traffic prediction and resource allocation were reported. Among them, adaptive time-domain prediction using the least-mean-square (LMS) algorithm is of particular interest because of its simplicity and relatively good performance. Although it improves the utilization of networks, its prediction delay and slow convergence degrade its performance of prediction and further processing. In this paper authors exploit the periodical dependence characteristics of MPEG video and develop a novel coding model based LMS algorithm to predict the bandwidth required by the future frame and group of pictures (GOP). It integrates the fixed structural information of coding model into the prediction. For any kind of frames (I, P, B frames), the algorithm can modify its parameters automatically using the coding information without considering different frames separately. Compared to the LMS, the modified LMS can predict the bit rate of frames fast and accurately without any delay. Through the analysis of queuing of video traffic in buffer of a renegotiate constant bit rate network, prediction for dynamic allocating bandwidth exhibits good performance than fixed bandwidth allocation.