基于可用带宽和延迟的多媒体流链路质量预测

Lim Su Jin, S. Lee, Simon Boung-Yew Lau, E. Karuppiah
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引用次数: 2

摘要

可用带宽和延迟等网络性能指标对于在多媒体流中实现良好的服务质量(QoS)至关重要。媒体应用程序的网络性能指标有独特的要求,例如音频会议、视频流、视频会议和高清(HD)视频会议。本文主要研究基于链路质量预测的电话会议类型建议。链路的质量是根据两个网络节点之间的可用带宽和延迟来分类的。我们实现并比较了两种最流行的基于监督学习的分类方法,即逻辑回归和支持向量机(SVM)。我们比较了两种方法的性能以及它们在链路质量预测中的适用性。实验结果表明,支持向量机在二分类和多分类的准确率上优于逻辑回归。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Link quality prediction for multimedia streaming based on available bandwidth and latency
Network performance metrics such as available bandwidth and latency are essential to achieve good Quality of Service (QoS) in multimedia streaming. There are unique requirements in network performance metrics for media applications, such as audio conferencing, video streaming, video conferencing, and high-definition (HD) video conferencing. In this paper, we focus on conference call type suggestion based on link quality prediction. The link's quality is classified based on the available bandwidth and latency between two network nodes. We have implemented and compared two of the most popular supervised learning based classification methods, i.e. logistic regression and support vector machine (SVM). We have compared the performance of both methods and their suitability to apply in link quality prediction. The experimental results show that SVM outperforms logistic regression for binary and multiclass classification in terms of accuracy.
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