Modeling and Analysis of Skype Video Calls: Rate Control and Video Quality
Modeling and Analysis of Skype Video Calls: Rate Control and Video Quality
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DOI：
10.1109/tmm.2013.2247988
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发表时间：
2013-10
影响因子：
7.3
通讯作者：
Xinggong Zhang;Yang Xu;Hao Hu;Yong Liu;Zongming Guo;Yao Wang
中科院分区：
文献类型：
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作者：
Xinggong Zhang;Yang Xu;Hao Hu;Yong Liu;Zongming Guo;Yao Wang
视频会议最近获得了发展势头，并被终端消费者广泛采用。但是，目前很少有研究在不同的网络条件下，视频通话的网络影响和用户体验质量（QoE）。本文研究了Skype视频通话的码率控制和视频质量，分析了大规模网络中的网络影响。我们首先在一个受控的网络测试平台上测量Skype视频通话的行为。通过改变数据包丢失率、传播延迟和可用网络带宽，我们观察了Skype如何调整其发送速率、FEC冗余、视频速率和帧速率。研究发现，Skype对轻微的数据包丢失和传播延迟具有鲁棒性，并且可以有效地利用可用的网络带宽。我们还发现，它采用了过于积极的FEC保护策略。基于测量结果，我们开发了Skype视频通话的速率控制模型、FEC模型和视频质量模型。从模型外推，我们进行数值分析，研究网络的影响。我们证明，用户退避质量下降时，作为一个有效的用户级速率控制方案。我们还表明，Skype视频通话确实是TCP友好的，并在网络过载时迅速响应拥塞。通过一个4G无线网络的案例研究，我们证明了所提出的模型可以用于用户QoE感知的网络配置。
Video-conferencing has recently gained its momentum and is widely adopted by end-consumers. But there have been very few studies on the network impacts of video calls and the user Quality-of-Experience (QoE) under different network conditions. In this paper, we study the rate control and video quality of Skype video call, and analyze the network impacts in large-scale networks. We first measure the behaviors of Skype video call on a controlled network testbed. By varying packet loss rate, propagation delay and available network bandwidth, we observe how Skype adjusts its sending rate, FEC redundancy, video rate and frame rate. It is found that Skype is robust against mild packet losses and propagation delays, and can efficiently utilize the available network bandwidth. We also find that it employs an overly aggressive FEC protection strategy. Based on the measurement results, we develop rate control model, FEC model, and video quality model for Skype video calls. Extrapolating from the models, we conduct numerical analysis to study the network impacts. We demonstrate that user back-offs upon quality degradation serve as an effective user-level rate control scheme. We also show that Skype video calls are indeed TCP-friendly and respond to congestion quickly when the network is overloaded. Through a case study of a 4G wireless network, we demonstrate that the proposed models can be used in user-QoE-aware network provisioning.