基于多重分形的VBR视频流量多步预测方法  

Multi-Step Prediction of VBR Video Traffic Based on Mutifractal Analysis

作　　者：王升辉[1] 裘正定[1] 

机构地区：[1]北京交通大学信息科学研究所,北京100044

出　　处：《计算机研究与发展》2007年第1期92-98,共7页Journal of Computer Research and Development

基　　金：国家"九七三"重点基础研究发展规划基金项目(2007CB307100)

摘　　要：视频流量的实时预测是进行网络资源优化和端到端QoS策略设计的重要前提.然而,目前基于短相关(SRD)的预测模型并不能对非平稳且具有长相关(LRD)和分形特性的视频流量进行有效的预测.分析发现,通过多重分形尺度间系数的相关性,可以把难以直接预测的LRD流量序列转化为可以用SRD模型预测的短相关序列组.基于多重分形的预测算法合理地利用了原始序列的LRD信息,具有很好的多步预测性能.Real-time prediction of video source traffic is an important step in network resource management and end-to-end quality-of-service （QoS） strategies. However, together with the long-range-dependence （LRD） and the traffic non-stationarity, it suggests that conventional prediction tools, which only use short- range dependence （SRD）, are not appropriate for VBR video traffic prediction. In this paper, by analyzing the correlation structure of multifractal coefficients, the original LRD trace can be converted to a series of SRD sequence in multifractal domain. Because the LRD feature of trace is used, the multi-step performance of proposed multifractal model is much better than traditional methods.

关 键 词：视频传输 流量预测 多重分形 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]