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Evaluating Wi-Fi Performance for VR Streaming: A Study on Realistic HEVC Video Traffic

Ferran Maura, Francesc Wilhelmi, Boris Bellalta

TL;DR

This paper addresses the challenge of delivering cloud-based VR streaming over Wi‑Fi by building a modular emulation framework that injects real HEVC traffic into an 802.11be model. It enables joint analysis of codec settings (e.g., GoP, FPS, bitrate) and network conditions (multiple users, distance, and PHY rates) on VR latency and quality. Key findings show that Intra-refresh coding reduces latency variability and supports up to four concurrent VR users at 100 Mbps before channel saturation, with trade-offs between perceptual quality (VMAF) and frame-size variability. The work provides practical guidance for configuring VR encoding and wireless parameters in dense deployments and sets the stage for an open testbed to explore advanced Wi‑Fi coordination strategies.

Abstract

Cloud-based Virtual Reality (VR) streaming presents significant challenges for 802.11 networks due to its high throughput and low latency requirements. When multiple VR users share a Wi-Fi network, the resulting uplink and downlink traffic can quickly saturate the channel. This paper investigates the capacity of 802.11 networks for supporting realistic VR streaming workloads across varying frame rates, bitrates, codec settings, and numbers of users. We develop an emulation framework that reproduces Air Light VR (ALVR) operation, where real HEVC video traffic is fed into an 802.11 simulation model. Our findings explore Wi-Fi's performance anomaly and demonstrate that Intra-refresh (IR) coding effectively reduces latency variability and improves QoS, supporting up to 4 concurrent VR users with Constant Bitrate (CBR) 100 Mbps before the channel is saturated.

Evaluating Wi-Fi Performance for VR Streaming: A Study on Realistic HEVC Video Traffic

TL;DR

This paper addresses the challenge of delivering cloud-based VR streaming over Wi‑Fi by building a modular emulation framework that injects real HEVC traffic into an 802.11be model. It enables joint analysis of codec settings (e.g., GoP, FPS, bitrate) and network conditions (multiple users, distance, and PHY rates) on VR latency and quality. Key findings show that Intra-refresh coding reduces latency variability and supports up to four concurrent VR users at 100 Mbps before channel saturation, with trade-offs between perceptual quality (VMAF) and frame-size variability. The work provides practical guidance for configuring VR encoding and wireless parameters in dense deployments and sets the stage for an open testbed to explore advanced Wi‑Fi coordination strategies.

Abstract

Cloud-based Virtual Reality (VR) streaming presents significant challenges for 802.11 networks due to its high throughput and low latency requirements. When multiple VR users share a Wi-Fi network, the resulting uplink and downlink traffic can quickly saturate the channel. This paper investigates the capacity of 802.11 networks for supporting realistic VR streaming workloads across varying frame rates, bitrates, codec settings, and numbers of users. We develop an emulation framework that reproduces Air Light VR (ALVR) operation, where real HEVC video traffic is fed into an 802.11 simulation model. Our findings explore Wi-Fi's performance anomaly and demonstrate that Intra-refresh (IR) coding effectively reduces latency variability and improves QoS, supporting up to 4 concurrent VR users with Constant Bitrate (CBR) 100 Mbps before the channel is saturated.
Paper Structure (17 sections, 4 figures, 1 table)

This paper contains 17 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Cloud-based VR streaming
  3. Server-Client Architecture
  4. Video Coding for Real-Time Streaming
  5. Requirements for High QoE
  6. Emulation Framework
  7. VR Server
  8. VR Client
  9. Wi-Fi Network
  10. System Model
  11. VR Emulation Framework Setup
  12. Performance Metrics Considered
  13. Results
  14. Effect of GoP size, , and bitrate on video quality and latency
  15. Effect of an increasing number of users
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Diagram of our proposed framework for emulating multiple streaming sessions.
  • Figure 2: Distribution of frame sizes (top), VMAF scores (middle), and VF-RTT (bottom) for 6 bitrates, with different and choices for two video samples streamed at 90 FPS.
  • Figure 3: Network performance per number of users, shown through (a) VF-RTT distribution, FLR average, (b) CU.
  • Figure 4: Distribution of VF-RTT of two users, one placed at a fixed location and a second one moving away from the . (a) User #1 is at 1.5 m from the AP, (b) User #1 is at 11 m from the AP.