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

TL;DR

This paper extends ALVR with a comprehensive set of metrics that provide a robust characterization of the network state, enabling more informed bitrate adjustments and develops and test the Network-aware Step-wise ABR algorithm for VR streaming (NeSt-VR), verifying the accuracy of the implemented network performance metrics and demonstrating NeSt-VR's adaptation capabilities.

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.

Figures (7)

• Figure 1: ALVR streaming process.
• Figure 2: ALVR v20.6.0 traffic using CBR at 100 Mbps, 90 fps, UDP, and SteamVR Home. Derived from parsed Wireshark traffic traces. Packet length includes transport headers.
• Figure 3: Testbed components.
• Figure 4: NeSt-VR algorithm diagram.
• Figure 5: Temporal evolution and Empirical Cumulative Distribution Functions (ECDF) of our metrics under a bandwidth-limited test (see $1$ in Tbl. \ref{['table:emulation_tests']}), comparing the values logged in ALVR with those independently derived from Wireshark (WS) traces. Temporal evolutions are filtered using a 16-sample sliding window average to enhance visibility, except for packet loss (discrete) and FOWD (implicitly filtered).