Cloud-gaming:Analysis of Google Stadia traffic

TL;DR

This paper analyzes Google Stadia's cloud gaming traffic, revealing its characteristics, protocol usage, and how it adapts to network changes, providing insights for network planning and resource management.

Contribution

It offers a comprehensive traffic measurement and analysis of Stadia, including protocol identification, traffic patterns, and a traffic model for simulations, which is novel in cloud gaming research.

Findings

Stadia traffic involves specific protocols for signaling and media content.

Traffic patterns vary with game type, resolution, and codec.

Stadia adapts to network capacity changes and exhibits distinct characteristics from other streaming services.

Abstract

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.