NeSt-VR: An Adaptive Bitrate Algorithm for Virtual Reality Streaming over Wi-Fi

TL;DR

NeSt‑VR tackles the challenge of real‑time interactive VR streaming over Wi‑Fi by introducing a VR‑aware ABR that relies on network health indicators, notably VR‑frame delivery reliability and VF‑RTT, to adapt video bitrate in small, controlled steps. Implemented as a fork of ALVR, NeSt‑VR adds a rich set of VR‑specific metrics and a hierarchical decision process that constrains bitrate using a validated capacity estimate and thresholds, enabling smooth quality transitions during capacity fluctuations, mobility, and OBSS interference. The authors validate the approach with extensive emulated experiments and CREW real‑world tests, showing reduced VF‑RTT, higher frame delivery rates, and lower packet loss compared with CBR and the native Adaptive ABR, in single‑ and multi‑user scenarios. The work provides an open‑source platform and a detailed metrics framework to benchmark and extend VR ABR solutions, with practical relevance for future Wi‑Fi evolutions and VR deployments in dense environments.

Abstract

Real-time interactive Virtual Reality (VR) streaming is a significantly challenging use case for Wi-Fi given its high throughput and low latency requirements, especially considering the constraints imposed by the possible presence of other users and the variability of the available bandwidth. Adaptive BitRate (ABR) algorithms dynamically adjust the encoded bitrate in response to varying network conditions to maintain smooth video playback. In this paper, we present the Network-aware Step-wise ABR algorithm for VR streaming (NeSt-VR), a configurable algorithm implemented in Air Light VR (ALVR), an open-source VR streaming solution. NeSt-VR effectively adjusts video bitrate based on real-time network metrics, such as frame delivery rate, network latency, and estimated available bandwidth, to guarantee user satisfaction. These metrics are part of a comprehensive set we integrated into ALVR to characterize network performance and support the decision-making process of any ABR algorithm, validated through extensive emulated experiments. NeSt-VR is evaluated in both single- and multi-user scenarios, including tests with network capacity fluctuations, user mobility, and co-channel interference. Our results demonstrate that NeSt-VR successfully manages Wi-Fi capacity fluctuations and enhances interactive VR streaming performance in both controlled experiments at UPF's lab and professional tests at CREW's facilities.

Figures (13)

• 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: Network topologies of UPF’s lab testbed.
• Figure 4: Single-user (and multi-user) setup in CREW's facilities.
• 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).