# Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurement

arXiv: https://arxiv.org/abs/2509.19669 · HTML: https://mlsanity.com/paper/2509.19669/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation](https://mlsanity.com/paper/2310.08543/) (2023)
- [Measuring the Performance and Network Utilization of Popular Video Conferencing Applications](https://mlsanity.com/paper/2105.13478/) (2021)
- [SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity](https://mlsanity.com/paper/2008.04687/) (2020)
- [A network analysis on cloud gaming: Stadia, GeForce Now and PSNow](https://mlsanity.com/paper/2012.06774/) (2020)
- [Characterizing User Platforms for Video Streaming in Broadband Networks](https://mlsanity.com/paper/2408.16995/) (2024)
- [Traffic Refinery: Cost-Aware Data Representation for Machine Learning on Network Traffic](https://mlsanity.com/paper/2010.14605/) (2020)
- [Estimating WebRTC Video QoE Metrics Without Using Application Headers](https://mlsanity.com/paper/2306.01194/) (2023)
- [Network Anatomy and Real-Time Measurement of Nvidia GeForce NOW Cloud Gaming](https://mlsanity.com/paper/2401.06366/) (2024)
- [MetaVRadar: Measuring Metaverse Virtual Reality Network Activity](https://mlsanity.com/paper/2402.08286/) (2024)
- [FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets](https://mlsanity.com/paper/2504.02174/) (2025)
- [Delay Sensitivity Classification of Cloud Gaming Content](https://mlsanity.com/paper/2004.05609/) (2020)
- [An experimental evaluation and characterization of VoIP over an LTE-A network](https://mlsanity.com/paper/2009.12580/) (2020)
- [Measuring and Estimating Key Quality Indicators in Cloud Gaming services](https://mlsanity.com/paper/2212.14073/) (2022)

## Abstract (arXiv)

To tap into the growing market of cloud gaming, whereby game graphics is rendered in the cloud and streamed back to the user as a video feed, network operators are creating monetizable assurance services that dynamically provision network resources. However, without accurately measuring cloud gaming user experience, they cannot assess the effectiveness of their provisioning methods. Basic measures such as bandwidth and frame rate by themselves do not suffice, and can only be interpreted in the context of the game played and the player activity within the game. This paper equips the network operator with a method to obtain a real-time measure of cloud gaming experience by analyzing network traffic, including contextual factors such as the game title and player activity stage. Our method is able to classify the game title within the first five seconds of game launch, and continuously assess the player activity stage as being active, passive, or idle. We deploy it in an ISP hosting NVIDIA cloud gaming servers for the region. We provide insights from hundreds of thousands of cloud game streaming sessions over a three-month period into the dependence of bandwidth consumption and experience level on the gameplay contexts.