From 2D to Next Generation VR/AR Videos: Enabling Efficient Streaming via QoE-aware Mobile Networks

Abstract

Ranging from traditional video streaming to Virtual Reality (VR) videos, the demand for video applications to mobile devices is booming. In the context of mobile operators a challenging problem is how to handle the increasing video traffic while managing the interplay between infrastructure optimization and QoE. Solving this issue is remarkably difficult, and recent investigations do not consider large-scale networks. In this dissertation paper we explore the solution space of efficient video streaming over mobile networks. First, we propose a model to predict video streaming quality based on the observation of performance indicators of the underlying IP network. Second, we introduce a novel QoE-aware path deployment heuristic for large-scale SDN-based mobile networks. Third, based on the lessons learned with QoE prediction for traditional video streaming, we finally explore the VR video domain by proposing PERCEIVE and VR-EXP. PERCEIVE is a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. In turn, VR-EXP consists of an experimentation platform that allows in-depth evaluation of state-of-the-art VR video optimization techniques. Obtained results show that the combination of the proposed methods for QoE-aware path selection outperformed state-of-the-art approaches.