Graph Theory-Based QoE-Driven Cooperation Stimulation for Content Dissemination in Device-to-Device Communication

摘要

With multimedia dominating the digital contents, device-to-device communication has been proposed as a promising data offloading solution in the big data area. As the quality of experience (QoE) is a major determining factor in the success of new multimedia applications, we propose a QoE-driven cooperative content dissemination (QeCS) scheme in this paper. In particular, all users predict the QoE of the potential connections characterized by the mean opinion score (MOS), and send the results to the content provider (CP). Then, the CP formulates a weighted directed graph according to the network topology and MOS of each potential connection. In order to stimulate cooperation among the users, the content dissemination mechanism is designed through seeking one-factor of the weighted directed graph with the maximum weight thus achieving maximum total user MOS. In addition, a debt mechanism is adopted to combat the cheat attacks. Furthermore, we extend the proposed QeCS scheme by considering a constrained condition to the optimization problem for fairness improvement. Extensive simulation results demonstrate that the proposed QeCS scheme achieves both efficiency and fairness especially in large scale and density networks.