Dissecting the performance of VR video streaming through the VR-EXP experimentation platform

To cope with the massive bandwidth demands of Virtual Reality (VR) video streaming, both the scientific community and the industry have been proposing optimization techniques such as viewport-aware streaming and tile-based adaptive bitrate heuristics. As most of the VR video traffic is expected to be delivered through mobile networks, a major problem arises: both the network performance and VR video optimization techniques have the potential to influence the video playout performance and the Quality of Experience (QoE). However, the interplay between them is neither trivial nor has it been properly investigated. To bridge this gap, in this article, we introduce VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation. Furthermore, we consolidate a set of relevant VR video streaming techniques and evaluate them under variable network conditions, contributing to an in-depth understanding of what to expect when different combinations are employed. To the best of our knowledge, this is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances. Extensive evaluations carried out using realistic datasets demonstrate that VR-EXP is instrumental in providing valuable insights regarding the interplay between network performance and VR video streaming optimization techniques

From 2D to next generation VR/AR videos : enabling efficient streaming via QoE-aware mobile networks

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