Wireless multimedia (e.g., video) content delivery in crowded venues (e.g., sport arenas and lecture halls) is challenging due to lack of spectrum. When multiple users are interested in the same content, WiFi multicast provides a potential solution. However, ensuring reliable and efficient delivery requires dynamically tuning parameters such as transmission rate and error correction based on the receivers’ feedback about their channel conditions. Specifically, for multicast, receiving accurate feedback from hundreds or thousands of receivers is infeasible. Therefore, the practicality of WiFi multicast for multimedia content distribution has been limited.
In a joint project of the WiMNet Lab and a Bell Labs team (led by Dr. Yigal Bejerano) we have been developing the AMuSe (Adaptive Multicast Services) system. AMuSe combines methods for collecting accurate feedback information with low overhead and for network adaptation (e.g., transmission rate) based on this feedback. Specifically, the system includes 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. Moreover, it includes schemes for dynamic rate adaptation based on the collected feedback.
As outlined in the papers below, we implemented the AMuSe system in the ORBIT testbed and have been performing extensive experiments to evaluate its performance with 150-200 WiFi receivers. Based on our large-scale experiments on ORBIT, we developed an interactive web-based demo to show the performance of AMuSe at scale. The demo won the second prize in the NYC Media Lab 2015 Summit, from about 100 demos (for more details, see the SEAS news item). The video below illustrates the demo, where most of the nodes are “high quality nodes” and the proportion of “low quality nodes” is very low.
In addition the WiFi implementations, we have also been focusing on LTE Multicast. Specifically, we developed the Dynamic Monitoring (DyMo) system for low-overhead feedback collection from thousands of receivers to effectively monitor large evolved Multimedia Broadcast/Multicast Services (eMBMS) deployments.
Y. Bejerano, C. Raman, C.-N. Yu, V. Gupta, C. Gutterman, T. Young, H. Infante, Y. Abdelmalek, and G. Zussman, “DyMo: Dynamic Monitoring of large scale LTE-multicast systems,” IEEE/ACM Transactions on Networking, vol. 27, no. 1, pp. 258–271, Feb. 2019.
V. Gupta, C. Gutterman, Y. Bejerano, and G. Zussman, “Experimental evaluation of large scale WiFi multicast rate control,” IEEE Transactions on Wireless Communications, vol. 17, no. 4, pp. 2319–2332, Apr. 2018.
Y. Bejerano, C. Raman, C.-N. Yu, V. Gupta, C. Gutterman, T. Young, H. Infante, Y. Abdelmalek, and G. Zussman, “DyMo: Dynamic Monitoring of large scale LTE-multicast systems,” in Proc. IEEE INFOCOM’17, 2017.
V. Gupta, Y. Bejerano, C. Gutterman, J. Ferragut, K. Guo, T. Nandagopal, and G. Zussman, “Light-weight feedback mechanism for WiFi multicast to very large groups – experimental evaluation,” IEEE/ACM Transactions on Networking, vol. 24, no. 6, pp. 3826–3840, Dec. 2016.
V. Gupta, R. Norwitz, S. Petridis, C. Gutterman, G. Zussman, and Y. Bejerano, “AMuSe: Large-scale WiFi video distribution - Experimentation on the ORBIT testbed,” in Demo description in Proc. IEEE INFOCOM’16, 2016.
Y. Bejerano, J. Ferragut, K. Guo, V. Gupta, C. Gutterman, T. Nandagopal, and G. Zussman, “Experimental evaluation of a scalable WiFi multicast scheme in the ORBIT testbed,” in Proc. 3rd GENI Research and Educational Experiment Workshop (GREE2014), 2014.