Modeling and Understanding the Quality of Experience of Online Mobile Gaming Services

Abstract

Mobile gaming has the largest market shares of all gaming domains, accounting for an estimated $77.2 billion in 2020. In recent times, one can witness an increase in highly interactive mobile online games. However, the gaming Quality of Experience (QoE) can be strongly influenced by network degradations, concretely by delay and packet loss. Thus, network providers need to ensure fast and reliable connections between the gaming servers and the users' clients. To maintain a satisfying user experience, QoE prediction models are fundamental. Aiming at the development of such a model, a detailed parameter space consisting of various delay and packet loss conditions will be investigated in this paper. Here, especially the importance of jitter is of interest. Next, it will be examined whether a recently published opinion model for cloud gaming, the ITU-T Rec. G.1072, can also be used for online mobile gaming. Finally, a new proposal for a model targeting online mobile gaming services will be presented and evaluated concerning its performance.

SmartORC: smart orchestration of resources in the compute continuum

“…For example, interactive services such as mobile games, in which no video is streamed over the network, are most prone to network depredations, including delay, packet loss, and jitter. A proper prediction of these values, and the consequent QoE-aware orchestration, is still an ongoing challenge (Schmidt et al, 2021). Since a complete QoE discussion would bring us far from the article's focus on orchestration, we have restricted the following discussion to QoS parameters on the tacit assumption that an analytic model of the expected QoE for an application can be assessed starting from QoS parameters, those that the orchestration can control.…”

The EXtended Reality Quality Riddle: A Technological and Sociological Survey

“…In [23], ITU-T addressed a QoE prediction model for gaming services. A QoE model for mobile gaming in presence of communication channel errors and delays appears in [24], whereas network latency is addressed in [25]. XR service QoE evaluation is also challenging since the perceived quality depends on the content, the user gender, the daily exposure time, at least at medium to high delivery quality (low delay) [19].…”

Weighted neighborhood classifier based on fuzzy neighborhood rough sets and its application to evaluate quality of experience for online gaming

“…Furthermore, there is also research on the influence of user and contextual factors on the QoE of online gaming in addition to the traditional network metrics 3,4 . There are currently two main categories of modeling approaches for QoE evaluation models of online gaming: statisticallybased methods and machine learning-based methods [5][6][7][8] . With respect to the QoE evaluation for other services, related methods based on extended approximate models have been applied and shown to be effective for QoE evaluation 9,10 .…”