A Stacking Learning-Based QoE Model for Cloud Gaming
Daniel Júnio Figueiredo Soares, Marcos Carvalho, Daniel Fernandes Macedo · 2023
Cloud gaming is a new paradigm that allows more cost-effective gaming for both users and game developers. The market is expected to grow 50-60% annually, reaching 22 billion USD by 2030. Gaming providers and ISPs require models of user satisfaction in order to improve their management of the cloud and network infrastructure. This paper analyses and proposes models that estimate the QoE of cloud gaming. Such models take as features network and game metrics. We assume an information sharing agreement among the cloud gaming platform and the ISP, allowing for a richer dataset. Data collection is performed with real users playing on a realistic testbed using similar protocols of the NVIDIA Geforce Now cloud gaming platform. We use stacking learning in order to improve the accuracy of the models, making a search for the best models and stacking them. We tested various improvements to the models, such as removing users with very low number of matches. Experiments show that models with more experienced players obtained a better precision, achieving 36.08%. When considering a range of plus or minus one within the estimated precision, the hit ratio was 86.56%. We also analyzed the model’ s sensitivity to inputs using feature importance analysis.