Empowering ISPs with Cloud Gaming User Experience Modeling: A NVIDIA GeForce NOW Use-Case.

Cloud gaming has emerged as a cost-effective and accessible gaming solution, with platforms like NVIDIA GeForce NOW leading the way. The rapid growth of this industry, projected to reach 6.8 billion USD by 2028, has sparked the need for enhanced user experience models to optimize cloud and network infrastructure. In our study, we conducted a comprehensive analysis of the in-game performance of the popular NVIDIA GeForce NOW cloud gaming platform under varying network conditions. Our research focused on quality of service (QoS) metrics, particularly the WebRTC logs, and their relationship with user experience, defined as in-game performance. Standardized and repeatable measurements from the GeForce NOW platform were used, where the player was asked to complete training exercises of fast-paced games under different network conditions. This paper analyses and proposes machine learning (ML) models that estimate the user experience of cloud gaming. The models are trained on the low-level network- and application-related QoS metrics extracted from WebRTC logs. Our contribution demonstrates that ML models can accurately estimate in-game performance from QoS parameters, highlighting network latency's greater impact on the player's gaming experience than packet loss, bandwidth, and jitter. With our novel model, internet service providers (ISPs) can effectively estimate user experience using only network-related metrics, enabling network optimization and enhancing gaming services. This research deepens our understanding of cloud gaming user experience and offers insights for refining cloud gaming services.