Adaptive video multicasting has been widely studied due to its effectiveness in transmitting live videos over wireless networks. The key challenges in live video multicasting include how to properly form multicast groups, select video versions and allocate wireless resources, in order to guarantee the quality of experience (QoE) while ensuring low latency delivery. Few prior works have jointly considered the above three problems, which are actually coupled. In this paper, to address these challenges, a novel multicast framework that leverages the advantages of network-assisted dynamic adaptive streaming over HTTP (DASH) and cloud radio access networks (CRANs) is proposed, where a multicast assistant server is deployed at the edge of a mobile network. Under this architecture, a joint user grouping, version selection, and bandwidth allocation method is designed to optimize the sum of user utilities. In particular, a two-step scheme is proposed to solve this complex problem. The number of multicast groups is first automatically determined and a user clustering method is presented. Then, group-level version selection and spectrum assignment algorithms are performed at different time scales. Simulation results demonstrate that our proposed scheme has at least a 7% improvement of QoE over the best performance of all baseline methods.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Live video streaming
- Resource allocation
- User grouping