NEWCAST: Anticipating Resource Management and QoE Provisioning for Mobile Video Streaming

TL;DR

NEWCAST is a framework that anticipates throughput variations in mobile networks to optimize video streaming, improving user QoE and system efficiency using real-world data and prototype testing.

Contribution

It introduces a novel framework that predicts throughput changes for better resource management and QoE provisioning in mobile video streaming.

Findings

NEWCAST improves efficiency and robustness for 5G architectures.

Prototype implementation demonstrates scalability over baseline algorithms.

Numerical results confirm enhanced QoE and system utilization.

Abstract

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.