Light-Weight Feedback Mechanism for WiFi Multicast to Very Large Groups—Experimental Evaluation
Light-Weight Feedback Mechanism for WiFi Multicast to Very Large Groups—Experimental Evaluation
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针对超大型组的 WiFi 组播的轻量级反馈机制——实验评估
DOI：
10.1109/tnet.2016.2560806
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
G. Zussman
中科院分区：
文献类型：
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作者：
Varun Gupta;Yigal Bejerano;Craig L. Gutterman;Jaime Ferragut;Katherine Guo;T. Nandagopal;G. Zussman
WiFi网络已经在全球部署，并且大多数移动的设备目前都支持WiFi。虽然已经提出了WiFi用于多媒体内容分发，但是其缺乏对多播服务的足够支持，这阻碍了其向大量设备提供多媒体内容分发的能力。在本文中，我们提出了AMuSe系统，其目标是使可扩展的和自适应的WiFi组播服务。AMuSe基于准确的接收器反馈，并且引起小的控制开销。特别是，我们开发了一种算法，用于动态选择的一个子集的多播接收器作为反馈节点，定期发送有关信道质量的信息的多播发送器。该反馈信息可以被多播发送器用于优化多播服务质量，例如，通过动态调整传输比特率。AMuSe不需要对标准进行任何更改或对WiFi设备进行任何修改。我们在ORBIT测试平台上实施了AMuSe，并在大约200个WiFi设备的大组中评估了其性能，包括有干扰源和无干扰源。我们广泛的实验表明，AMuSe可以在密集的组播环境中提供准确的反馈。即使在外部干扰和网络条件变化的情况下，它也优于几种替代方案。
WiFi networks have been globally deployed and most mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In this paper, we present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe is based on accurate receiver feedback and incurs a small control overhead. In particular, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information about the channel quality to the multicast sender. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. AMuSe does not require any changes to the standards or any modifications to the WiFi devices. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices, both with and without interference sources. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions.