With recent standardization and deployment of LTE eMBMS, cellular multicast is gaining traction as a method of efficiently using wireless spectrum to deliver large amounts of multimedia data to multiple cell sites. Content delivery over cellular networks has been a challenge, with the last hop RAN being the bottleneck and a significant source of latency. CDNs relieve the backbone bandwidth utilization and reduce latency to deliver content to the end-user, but only provide a relatively small amount of relief. Similarly, traditional multicast solutions typically save resource consumption in the backbone (i.e., further up the multicast tree). With multicast on the cellular “last mile” – the RAN, the improvement in last-hop bottleneck utilization, end-to-end latency, and the cost of deploying resources to deliver content over cellular networks may be more effectively addressed.
Cellular operators seek methods of performing optimal resource allocation in eMBMS based on a complete understanding of the complex interactions between a number of mechanisms: the multicast coding scheme, the resources allocated to unicast users and their scheduling at the base stations, the resources allocated to a multicast group to satisfy the user experience of its members, and the number of groups and their membership.
Group partitioning and resource allocation: We study the optimal allocation of wireless resources for users to maximize proportional fair utility. To handle the heterogeneity of user channel conditions, we efficiently and optimally partition multicast users into groups so that users with good signal strength do not suffer by being grouped together with users of poor signal strength.
Local and global dynamics: The network operator allocates spectrum resources to users to achieve a globally fair and efficient distribution of rates. However, an individual user might selfishly maximize her rate by switching between multicast and unicast, deviating from the operator’s desired solution. We analyze the interaction between the globally fair solution and individual user’s desire to maximize its rate. We show that even if the user deviates from the global solution in a number of scenarios, we can bound the number of selfish users that will choose to deviate.