A Continuous-Time Markov decision process-based resource allocation scheme in vehicular cloud for mobile video services

The development of vehicular network technologies boosts the wide deployment of mobile video applications with high requirements of Quality of Experience (QoE) in the Fifth-Generation (5G) era. However, the limitation of computing capabilities of intelligent vehicles makes it difficult to meet the QoE demands. The offloading technique that is put forward in vehicular cloud can extend such limitations largely by offloading video processing tasks to cloud or other vehicles. On the other hand, the emerging mobile social networks create new patterns for mobile applications to serve people on the basis of social relations. The mobile video offloading services can also be influenced by social relations of users inside a cloudlet. Therefore, in this paper we study the impact of social graphs on mobile video offloading services and propose a Continuous-Time Markov Decision Process (CTMDP) based resource allocation scheme considering social graphs as constraints. By using relative value iteration algorithm, an optimal policy can be obtained, which aims at maximizing the average system rewards. Simulation results show that our CTMDP based scheme achieves an enhanced performance against Greedy benchmark under different metrics.