Cloud Gaming: A QoE Study of Fast-paced Single-player and Multiplayer Gaming

Sebastian Flinck Lindstrom, Markus Wetterberg, and Niklas Carlsson

Paper: Sebastian Flinck Lindstrom, Markus Wetterberg, and Niklas Carlsson, "Cloud Gaming: A QoE Study of Fast-paced Single-player and Multiplayer Gaming", Proc. IEEE/ACM International Conference on Utility and Cloud Computing (IEEE/ACM UCC), Dec. 2020. (pdf)

Abstract: Cloud computing offers an attractive solution for modern computer games. By moving the increasingly demanding graphical calculations (e.g., generation of real-time video streams) to the cloud, consumers can play games using small, cheap devices. While cloud gaming has many advantages and is increasingly deployed, not much work has been done to understand the underlying factors impacting players' user experience when moving the processing to the cloud. In this paper, we study the impact of the quality of service (QoS) factors most affecting the players' quality of experience (QoE) and in-game performance. In particular, these relationships are studied from multiple perspectives using complementing analysis methods applied on the data collected via instrumented user tests. During the tests, we manipulated the players' network conditions and collected low-level QoS metrics and in-game performance, and after each game, the users answered questions capturing their QoE. New insights are provided using different correlation/auto-correlation/cross-correlation statistics, regression models, and a thorough breakdown of the QoS metric most strongly correlated with the users' QoE. We find that the frame age is the most important QoS metric for predicting in-game performance and QoE, and that spikes in the frame age caused by large frame transfers can have extended negative impact as they can cause processing backlogs. The study emphasizes the need to carefully consider and optimize the parts making up the frame age, including dependencies between the processing steps. By lowering the frame age, more enjoyable gaming experiences can be provided.

Code and datasets

To help build upon our work, below, we make available code and example datasets.

Code: The code used in our paper is made available here (778 MB).
Datasets: The primary dataset analyzed in our paper is made available here Additional datasets may be added later.

Note: If you use our datafiles or code in your research, please include a reference to our UCC 2020 paper (pdf) in your work.