QoE Multi-Stage Machine Learning for Dynamic Video Streaming

Abstract

The rapid growth of video traffic in cellular networks is a crucial issue to be addressed by mobile operators. An emerging and promising trend in this regard is the development of solutions that aim at maximizing the quality of experience (QoE) of the end users. However, predicting the QoE perceived by the users in different conditions remains a major challenge. In this paper, we propose a machine learning approach to support QoE-based video admission control and resource management algorithms. More specifically, we develop a multi-stage learning system that combines the unsupervised learning of video features from the size of H.264-encoded video frames with a supervised classifier trained to automatically extract the quality-rate characteristics of unknown video sequences. This QoE characterization is then used to manage simultaneous video transmissions through a shared channel in order to guarantee a minimum quality level delivered to the final users. Simulation results show that the proposed video admission control and resource management algorithms, which are based on learning-based QoE classification of video sequences, outperform standard content-agnostic strategies.