Experimenting with Adaptive Bitrate Algorithms for Virtual Reality Streaming over Wi-Fi

TL;DR

This paper evaluates adaptive bitrate algorithms for VR streaming over Wi-Fi, introducing new network metrics and a novel algorithm to improve video quality and user experience amidst bandwidth fluctuations.

Contribution

It presents a comprehensive set of network metrics and the NeSt-VR algorithm, enhancing adaptive streaming performance for VR over Wi-Fi.

Findings

Network metrics accurately characterize Wi-Fi conditions.

NeSt-VR adapts bitrate effectively during bandwidth changes.

Improved VR streaming quality and stability.

Abstract

Interactive Virtual Reality (VR) streaming over Wi-Fi networks encounters significant challenges due to bandwidth fluctuations caused by channel contention and user mobility. Adaptive BitRate (ABR) algorithms dynamically adjust the video encoding bitrate based on the available network capacity, aiming to maximize image quality while mitigating congestion and preserving the user's Quality of Experience (QoE). In this paper, we experiment with ABR algorithms for VR streaming using Air Light VR (ALVR), an open-source VR streaming solution. We extend ALVR with a comprehensive set of metrics that provide a robust characterization of the network's state, enabling more informed bitrate adjustments. To demonstrate the utility of these performance indicators, we develop and test the Network-aware Step-wise ABR algorithm for VR streaming (NeSt-VR). Results validate the accuracy of the newly implemented network performance metrics and demonstrate NeSt-VR's video bitrate adaptation capabilities.