MMVS: Enabling Robust Adaptive Video Streaming for Wildly Fluctuating and Heterogeneous Networks

Abstract

With the advancement of wireless technology, the fifth-generation mobile communication network (5G) has the capability to provide exceptionally high bandwidth for supporting high-quality video streaming services. Nevertheless, this network exhibits substantial fluctuations, posing a significant challenge in ensuring the reliability of video streaming services. This research introduces a novel algorithm, the Multi-type data perception-based Meta-learning-enabled adaptive Video Streaming algorithm (MMVS), designed to adapt to diverse network conditions, encompassing 3G and mmWave 5G networks. The proposed algorithm integrates the proximal policy optimization technique with the meta-learning framework to cope with the gradient estimation noise in network fluctuation. To further improve the robustness of the algorithm, MMVS introduces meta advantage normalization. Additionally, MMVS treats network information as multiple types of input data, thus enabling the precise definition of distinct network structures for perceiving them accurately. The experimental results on network trace datasets in real-world scenarios illustrate that MMVS is capable of delivering an additional 6% average QoE in mmWave 5G network, and outperform the representative benchmarks in six pairs of heterogeneous networks and user preferences.