# Virtual Reality Gaming on the Cloud: A Reality Check

arXiv: https://arxiv.org/abs/2109.10114 · HTML: https://mlsanity.com/paper/2109.10114/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2021.

## Built on by (most cited first, 8 of 8)

- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [How Does Forecasting Affect the Convergence of DRL Techniques in O-RAN Slicing?](https://mlsanity.com/paper/2309.00489/) (2023)
- [Time-Distributed Feature Learning for Internet of Things Network Traffic Classification](https://mlsanity.com/paper/2409.05096/) (2024)
- [ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction](https://mlsanity.com/paper/2411.11894/) (2024)
- [SafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12753/) (2025)
- [Discern-XR: An Online Classifier for Metaverse Network Traffic](https://mlsanity.com/paper/2411.05184/) (2024)
- [Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction](https://mlsanity.com/paper/2506.19890/) (2025)
- [ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended Reality](https://mlsanity.com/paper/2609.04493/) (2026)

## Abstract (arXiv)

Cloud virtual reality (VR) gaming traffic characteristics such as frame size, inter-arrival time, and latency need to be carefully studied as a first step toward scalable VR cloud service provisioning. To this end, in this paper we analyze the behavior of VR gaming traffic and Quality of Service (QoS) when VR rendering is conducted remotely in the cloud. We first build a VR testbed utilizing a cloud server, a commercial VR headset, and an off-the-shelf WiFi router. Using this testbed, we collect and process cloud VR gaming traffic data from different games under a number of network conditions and fixed and adaptive video encoding schemes. To analyze the application-level characteristics such as video frame size, frame inter-arrival time, frame loss and frame latency, we develop an interval threshold based identification method for video frames. Based on the frame identification results, we present two statistical models that capture the behaviour of the VR gaming video traffic. The models can be used by researchers and practitioners to generate VR traffic models for simulations and experiments - and are paramount in designing advanced radio resource management (RRM) and network optimization for cloud VR gaming services. To the best of the authors' knowledge, this is the first measurement study and analysis conducted using a commercial cloud VR gaming platform, and under both fixed and adaptive bitrate streaming. We make our VR traffic data-sets publicly available for further research by the community.