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Scalable WiFi multicast services for very large groups

IEEE 802.11-based wireless local area networks, referred to as WiFi, have been globally deployed and the vast majority of 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. We propose AMuSe, a scalable and adaptive interference mitigation solution for WiFi multicast services which is based on accurate receiver feedback and that incurs a small control overhead. Specifically, we develop a scheme for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information, such as channel quality or received packet statistics, to the multicast sender. This feedback information is used by the multicast sender to optimize the multicast service quality, e.g., by dynamically adjusting the transmission bit-rate. Our proposed solution does not require any changes to the standards or any modifications to the WiFi devices. We have implemented the proposed solution in the ORBIT testbed and evaluated its performance in large groups with approximately 250 receivers, both with and without interference sources. Our online experiments demonstrate that our system provides practical multicast services that can accommodate hundreds of receivers.