Anticipating Resource Management and QoE Provisioning for Mobile Video Streaming

The knowledge of future throughput variation in wireless networks using smartphone becomes more and more possible by exploiting the rich contextual information from smartphone sensors through mobile applications and services. Contextual information may include the traffic, mobility and radio conditions. Inspired by the attractive features and potential advantages of this agile resource management, several approaches have been proposed during the last period. However, agile resource management also comes with its own challenges, and there are significant technical issues that still need to be addressed for successful rollout and operation of this technique. In this paper, we propose an approach for anticipating throughput variation for mobile video streaming services. The solution of the optimization problem realizes a fundamental trade-offs among critical metrics that impact the user's perceptual quality of the experience (QoE) and system utilization. Both simulated and real-world traces are carried out to evaluate the performance of the proposed approach. It is shown that our approach provides the accuracy, efficiency and robustness that the new 5G architectures require.