Resource allocation for cloud-based social TV applications using Particle Swarm Optimization

Social interaction of groups of users, amongst themselves and with the media content itself, is increasingly becoming popular due to the advancements in the Internet access technologies. However, multimedia resource provisioning for dispersed user groups poses a challenge and demands innovative technologies. This paper proposes a novel approach based on Particle Swarm Optimization (PSO) to optimally allocate computational and networking resources to a group of interactive users, such that the group Quality-of-Service (QoS) is maximized. We evaluate the performance of the proposed improved PSO method with respect to the state-of-the-art greedy resource allocation mechanisms and related PSO approaches. The ability to find a feasible solution (i.e., the serving probability) and the accuracy of such solutions are compared for different network topologies. The proposed method demonstrates reduced computational complexity, an up to 40% increase in the serving probability compared to the greedy methods, and up to 60 times faster convergence compared to the basic PSO approach. Overall, the comparable QoS level to the optimal solution suggests that the proposed solution efficiently allocates the resources available in the network.