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Modeling variable bit rate video on wired and wireless networks using discrete-item self-similar systems

机译:使用离散项目自我类似系统在有线和无线网络上建模可变比特率视频

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In recent years, wireless networks have gained a lot of interest due to the increasing demand for Internet based wireless applications. Due to bandwidth constraints video traffic over the Internet are compressed. This type of compressed traffic shows burstiness on different time scales. In particular, the phenomena of long-range dependence (LRD), self-similarity and heavy-tailed distributions appear to play a prominent role in modeling VBR video traffic. In this paper we present novel discrete-time models based on self-similar considerations to characterize the statistical properties of VBR video traffic. We analyze MPEG VBR video traces that were collected on a wired network as well as multimedia traffic obtained at the access points (AP) on a wireless LAN based on IEEE 802.11b implementation. The results show that these traffic traces display long-range dependence and heavy-tailed behavior and the discrete-time statistically self-similar model discussed here can not only generate traces with the measured Hurst parameter but also provide better fits to the autocovariance function of the timedomain data than the existing models such as Markovian, LRD and M/G/∞.
机译:近年来,由于基于互联网的无线应用的需求越来越大,无线网络已经获得了很多兴趣。由于带宽约束互联网上的视频流量被压缩。这种类型的压缩流量在不同的时间尺度上显示出突发。特别是,远程依赖性(LRD),自我相似性和重型分布的现象似乎在建模VBR视频流量中发挥着突出作用。在本文中,我们基于自我类似的考虑来表征VBR视频流量的统计特性的新颖离散时间模型。我们分析了在有线网络上收集的MPEG VBR视频迹线以及基于IEEE 802.11b实现的无线LAN上的接入点(AP)获得的多媒体流量。结果表明,这些交通迹线显示了远程依赖性和重尾行为,这里讨论的离散时间统计学上自类似的模型不仅可以生成带有测量的赫斯特参数的迹线,还可以为自动转换功能提供更好的拟合TimeDomain数据比Markovian,LRD和M / G /∞等现有型号。

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