Cloud-gaming:Analysis of Google Stadia traffic
Not on the map. Why?
Abstract (arXiv)
Interactive, real-time, and high-quality cloud video games pose a serious challenge to the Internet due to simultaneous high-throughput and low round trip delay requirements. In this paper, we investigate the traffic characteristics of Stadia, the cloud-gaming solution from Google, which is likely to become one of the dominant players in the gaming sector. To do that, we design several experiments, and perform an extensive traffic measurement campaign to obtain all required data. Our first goal is to gather a deep understanding of Stadia traffic characteristics by identifying the different protocols involved for both signalling and video/audio contents, the traffic generation patterns, and the packet size and inter-packet time probability distributions. Then, our second goal is to understand how different Stadia games and configurations, such as the video codec and the video resolution selected, impact on the characteristics of the generated traffic. We also evaluate the ability of Stadia to adapt to different link capacity conditions, including cases where the capacity drops suddenly, as well as sudden increases in the network latency. Our results and findings, besides illustrating the characteristics of Stadia traffic, are also valuable for planning and dimensioning future networks, as well as for designing new resource management strategies. Finally, we compare Stadia traffic to other video streaming applications, showcasing the main differences between them, and introduce a traffic model using our captures. We show that this model can be used in simulations to further investigate the network performance in presence of Stadia traffic.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Sep 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 2.
4 papers.
- 2025Network Traffic Analysis with Process Mining: The UPSIDE Case Studycited by 2
- 2023Video traffic identification with novel feature extraction and selection methodcited by 0
- 2024Combining Cloud and Mobile Computing for Machine Learningcited by 0
- 2025From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Geminicited by 0
- 2025Network Traffic Analysis with Process Mining: The UPSIDE Case Study10 Dec 2025
- 2025From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini13 Oct 2025
- 2024Combining Cloud and Mobile Computing for Machine Learning20 Jan 2024
- 2023Video traffic identification with novel feature extraction and selection method6 Mar 2023