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Design and analysis of optimal adaptive de-jitter buffers

机译:最佳自适应去抖动缓冲器的设计与分析

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In order to transfer voice or some other application requiring real-time delivery over a packet network, we need a de-jitter buffer to eliminate delay jitters. An important design parameter is the depth of the de-jitter buffer since it influences two important parameters controlling voice quality, namely voice-path delay and packet loss probability. In this paper, we propose and study several schemes for optimally adjusting the depth of the de-jitter buffer. In addition to de-jitter-buffer depth adjustments within a call, the initial value and rates of changes of the de-jitter buffer depth are allowed to depend on the class of the call and are adaptively adjusted (upwards or downwards) for every new call based on voice-path delay and packet loss probability measurements over one or more previous calls. Parameter adjustments are geared towards either (a) minimizing voice-path delay while maintaining a packet loss probability objective, or (b) maximizing R-factor, an objective measure of voice quality that depends both on the voice-path delay and the packet loss probability. Using simulation models and measured packet delay traces, it is shown that adaptive schemes perform better than static ones and adaptive schemes with learning perform better than ones without learning.
机译:为了在分组网络上传输语音或其他需要实时传递的应用程序,我们需要一个去抖动缓冲器以消除延迟抖动。一个重要的设计参数是去抖动缓冲区的深度,因为它会影响控制语音质量的两个重要参数,即语音路径延迟和丢包率。在本文中,我们提出并研究了几种用于最佳调整去抖动缓冲区深度的方案。除了在呼叫中进行去抖动缓冲器深度调整外,还允许去抖动缓冲器深度的初始值和变化率取决于呼叫的类别,并针对每个新呼叫进行自适应调整(向上或向下)。基于一个或多个先前呼叫的语音路径延迟和数据包丢失概率测量的呼叫。参数调整旨在(a)最小化语音路径延迟,同时保持丢包概率目标,或(b)最大化R因子,这是一种取决于语音路径延迟和丢包率的语音质量客观度量可能性。使用仿真模型和测得的数据包延迟轨迹,表明自适应方案的性能优于静态方案,有学习的自适应方案的性能优于无学习方案。

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