NEWCAST: Anticipating Resource Management and QoE Provisioning for Mobile Video Streaming
This work addresses resource management for mobile video streaming users, presenting an incremental improvement over existing adaptive bitrate algorithms.
The authors tackled the problem of mobile video streaming by proposing NEWCAST, a framework that anticipates throughput variations to optimize user quality of experience (QoE) and system utilization costs, showing efficiency in computational complexity and robustness for 5G architectures through simulations and real-world evaluations.
The knowledge of future throughput variations in mobile networks becomes more and more possible today thanks to the rich contextual information provided by mobile applications and services 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 an framework (called NEWCAST) that anticipates the throughput variations to deliver video streaming content. 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 collected from [1], were carried out to evaluate the performance of NEWCAST. In particular, we show 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 compared to baseline adaptive bitrate algorithms.