Transmission of variable bit rate (VBR) video, because of the burstiness of VBR video traffic, has high fluctuation in bandwidth requirement. Traffic smoothing algorithm is very efficient in reducing burstiness of the...Transmission of variable bit rate (VBR) video, because of the burstiness of VBR video traffic, has high fluctuation in bandwidth requirement. Traffic smoothing algorithm is very efficient in reducing burstiness of the VBR video stream by trans- mitting data in a series of fixed rates. We propose in this paper a novel segment-based bandwidth allocation algorithm which dynamically adjusts the segmentation boundary and changes the transmission rate at the latest possible point so that the video segment will be extended as long as possible and the number of rate changes can be as small as possible while keeping the peak rate low. Simulation results showed that our approach has small bandwidth requirement, high bandwidth utilization and low computation cost.展开更多
The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated ...The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.展开更多
VBR(Variab le B itRate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测是提高信息传输速度和提高网络带宽资源利用效率的重要手段.针对以上问题,本文提出了一种用于VBR视频通信量预测的差分输入支持向量机(S...VBR(Variab le B itRate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测是提高信息传输速度和提高网络带宽资源利用效率的重要手段.针对以上问题,本文提出了一种用于VBR视频通信量预测的差分输入支持向量机(SVM:SupportVectorM achine)网络模型.该网络模型采用结构风险最小化准则,在最小化经验风险的同时,尽量缩小模型预测误差的上界,从而使网络模型具有更好的推广能力.实验结果表明:支持向量机网络模型的预测误差为0.0018,而梯度径向基函数(G rad ient Rad ial Basis Function:GRBF)神经网络模型的预测误差为0.0029.可以看出,支持向量机网络模型的预测精度要比GRBF网络模型的预测精度高出大约40%.展开更多
基金Project supported by the Hi-Tech Research and Development Pro-gram (863) of China (No. 2003AA103810) and the National NaturalScience Foundation of China (No. 60332030)
文摘Transmission of variable bit rate (VBR) video, because of the burstiness of VBR video traffic, has high fluctuation in bandwidth requirement. Traffic smoothing algorithm is very efficient in reducing burstiness of the VBR video stream by trans- mitting data in a series of fixed rates. We propose in this paper a novel segment-based bandwidth allocation algorithm which dynamically adjusts the segmentation boundary and changes the transmission rate at the latest possible point so that the video segment will be extended as long as possible and the number of rate changes can be as small as possible while keeping the peak rate low. Simulation results showed that our approach has small bandwidth requirement, high bandwidth utilization and low computation cost.
文摘The alpha stable self-similar stochastic process has been proved an effective model for high variable data traffic. A deep insight into some special issues and considerations on use of the process to model aggregated VBR video traffic is made. Different methods to estimate stability parameter a and self-similar parameter H are compared. Processes to generate the linear fractional stable noise （LFSN） and the alpha stable random variables are provided. Model construction and the quantitative comparisons with fractional Brown motion （FBM） and real traffic are also examined. Open problems and future directions are also given with thoughtful discussions.
文摘VBR(Variab le B itRate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测是提高信息传输速度和提高网络带宽资源利用效率的重要手段.针对以上问题,本文提出了一种用于VBR视频通信量预测的差分输入支持向量机(SVM:SupportVectorM achine)网络模型.该网络模型采用结构风险最小化准则,在最小化经验风险的同时,尽量缩小模型预测误差的上界,从而使网络模型具有更好的推广能力.实验结果表明:支持向量机网络模型的预测误差为0.0018,而梯度径向基函数(G rad ient Rad ial Basis Function:GRBF)神经网络模型的预测误差为0.0029.可以看出,支持向量机网络模型的预测精度要比GRBF网络模型的预测精度高出大约40%.
