Assessing the Impact of Network Quality-of-Service on Metaverse Virtual Reality User Experience

TL;DR

The paper addresses how network QoS shapes user experience in metaverse VR, focusing on three QoE metrics (Freeze, Content Loading, and Control Responsiveness) across public hubs and private events in Rec Room, VRChat, and MultiverseVR on Oculus Quest 2. It uses a controlled lab setup to impose bandwidth, latency, and packet loss, and employs an ACR-based five-level scale to map QoS to QoE. Key findings show that Freeze is relatively robust to latency but highly sensitive to packet loss, while Content Loading and Control Responsiveness are more affected by bandwidth and latency, respectively; private events require substantially higher QoS than public hubs. The study provides concrete QoS thresholds for five QoE levels, offering ISPs actionable guidance to optimize networks for superior metaverse experiences and highlighting the importance of pre-installed content and motion-prediction in sustaining immersion. Overall, the work bridges a gap between network QoS engineering and metaverse VR user experience, with practical implications for network operators and metaverse platform designers.

Abstract

Metaverse virtual reality (VR) applications enable users to socialise, work, entertain, and study online with immersive experiences beyond the classic PC-based interactions. While the 360-degree immersion enables users to be fully engaged in a virtual scenario, suboptimal Quality-of-Experience (QoE) like poorly displayed 3D graphics, disruptive loading time, or motion lagging caused by degraded network Quality-of-Service (QoS) can be perceived by users much worse (such as dizziness) than a monitor visualisation. This paper empirically measures user QoE of metaverse VR caused by network QoS. Specifically, by focusing on both public social hubs and private user-created events in three popular metaverse VR applications (Rec Room, VRChat and MultiverseVR), we first identify three metrics, including environment freeze level, peripheral content loading time, and control response time, that describe metaverse user experience. By tuning three network QoS parameters (bandwidth, latency, and packet loss), we benchmark each QoE metric's level from excellent to unplayable. Key insights are revealed, such as freeze of metaverse virtual environment is resilient to latency but sensitive to packet loss, and private user-created events demand better network conditions than public social hubs, providing a reference for ISPs to optimise their network QoS for superlative metaverse user experience.

Figures (8)

• Figure 1: Our measurement setup in controlled lab environment.
• Figure 2: Screenshots of representative user experience issues including (a) long freeze of virtual environment, (b) excessive peripheral content loading time, and (c) movement jitters observed by the authors in three popular metaverse VR applications (Rec Room, VRChat and MultiverseVR).
• Figure 3: Bandwidth usage by one public social hub metaverse session with bandwidth constraints shown as red frames.
• Figure 4: Bandwidth usage by one private user-created metaverse event with bandwidth constraints shown as red frames.
• Figure 5: Number of packet loss per second in one public social hub metaverse session with various packet loss rates.