Spectrum Allocation and Bitrate Adjustment for Mobile Social Video Sharing: Potential Game with Online QoS Learning Approach

With the recent progress on mobile networking and devices, mobile social video sharing (MSVS) has emerged as one of the most important social media services. It enables mobile users to create ultra-short video clips and instantly share them with social friends. Due to the huge volume of videos and limited available bandwidth of wireless infrastructure, it is challenging to distribute these massive videos to mobile users with satisfactory quality of service (QoS). In this paper, we present a general framework to model the video diffusion among mobile users and user QoS of the MSVS service over the wireless infrastructure. Then, we utilize the hierarchical structure to decompose this problem into two subproblems, including a bitrate adjustment and spectrum allocation problems. For the bitrate adjustment problem, we propose a QoS estimation model based on the large deviation principle. By introducing a sliding window method to derive the online estimation, we develop an online bitrate adjustment strategy without relying on any prior knowledge of neither network environment nor video traffic. For the spectrum allocation problem, we prove that such a problem is a potential game. We devise a decentralized algorithm to find the Nash equilibrium, and analyze the convergence rate and the performance gap with the centralized optimization solution. Through extensive real trace driven simulations, we demonstrate that our proposed algorithm can guarantee smooth video playback with a higher PSNR.