NEWCAST: Joint Resource Management and QoE-Driven Optimization for Mobile Video Streaming
Résumé
Predicting future throughput in mobile networks becomes more and more possible today thanks to the rich contextual information provided by mobile applications and smartphone sensors. It is even likely that such contextual information, which may include traffic, mobility and radio conditions will lead to a novel agile resource management not yet thought of. In this paper, we propose a framework (called NEWCAST) that anticipates the throughput variations to deliver video streaming content. NEWCAST takes advantage of the capacity prediction in order to better distribute the resources allocated by the scheduler among users over the prediction horizon. This has the advantage of leading towards better user engagement for video streaming users without harming other traffics present in the system. We develop an optimization problem that realizes a fundamental trade-off among critical metrics that impact the user's perceptual quality of experience (QoE) and the cost of system utilization. Both simulated and real-world throughput traces were carried out to evaluate the performance of NEWCAST. It is shown from our numerical results that NEWCAST provides the efficiency that the new 5G architectures require in terms of computational complexity and robustness. We also implement a prototype system of NEWCAST and evaluate it in a real environment with a real player to show its efficiency and scalability in a multi-users scenario compared to baseline adaptive bitrate algorithms.
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