The rise of mobile cloud gaming  (MCG) has necessitated understanding its impact on mobile network design and deployment for end users' QoE maximization. MCG is a dynamic service that requires stringent quality from network operators. Therefore, this paper investigates the subjective QoE of MCG over mobile networks played on smartphones. We conducted subjective tests (N=31); our results indicate that MCG is affected differently by QoS attributes such as packet loss (PL), round trip time (RTT) and jitter compared to cloud games and online mobile games. We identify that RTT values above 100 milliseconds significantly impact users' QoE, measured via the mean opinion score (MOS). Further, lower RTT values with high PL; and higher RTT values with low PL cause a strong negative effect on MOS. Lastly, bursty jitter seems to affect the MOS, while random jitter does not significantly impact MOS.

Virtual reality cloud-based gaming (VRCG) services are becoming widely available on virtual reality (VR) devices delivered over computer networks.VRCG brings users worldwide an extensive catalog of games to play anywhere and anytime. Delivering these gaming services in existing broadband mobile networks is challenging due to their stochastic nature and the user perceived Quality of Experience (QoE)' sensitivity towards them. More research is needed regarding developing effective methods to measure the impact of network QoS factors on users' QoE in the VRCG context. Therefore, this paper proposes, develops, and validates three novel regression models trained on a real dataset collected via subjective tests (N=30); the dataset contains subjective users' QoE ratings regarding VR shooters affected by network conditions (N=28), such as round-trip time (RTT), random jitter (RJ), and packet loss (PL). Our findings reveal that due to the nonlinear relationship of (RTT and RJ) tested together, nonlinear(mean absolute error (MAE)=0.14) and polynomial (MAE=0.15) regression models have the best performance; yet, simple linear regression model(MAE=0.19) is also suitable to predict QoE for VRCG. Further, we found that the model's most important feature is RTT, followed by (RTT, RJ). Finally, our models' prediction of QoE for real-world traffic measurements suggests that mobile network traffic (4G, 5G non-standalone, 5G standalone) provides a 2.5 \(\leq MOS\_{QoE} \leq\) 3.0 experience for VRCG, while 4.2 \(\leqMOS\_{QoE} \leq\) 4.4  for wired connections, suggesting the need for improvements in the current commercial 5G network deployments to deliverVRCG.

Quality of Experience (QoE) subjective assessment often demands setting up expensive lab experiments that involve controlling several software programs and services. In addition, these experiments may pose significant challenges regarding man-agement of testbed software components as they may have to be synchronized for efficient data collection, leading to human errors or loss of time. Further, maintaining error-free repeatability between subsequent subjective tests and comprehensive data collection is essential. Therefore, this paper proposes, develops and validates ALTRUIST, a multi-platform tool that assists the experimenter in conducting subjective tests by controlling external applications, facilitates data collection and automates test execution for conducting repeatable subjective tests in broad application areas.

Cloud-based social eXtended Reality (XR) services are the cornerstone for realizing the promises of the Metaverse. These services hosted either on datacenters or edge, will demand stringent mobile network quality of service (QoS) to operate effectively and provide an acceptable user quality. It becomes fundamental to study how mobile networks QoS factors round-trip time (RTT), packet loss (PL), and jitter affect these services by measuring their effect on users' perceived quality of experience (QoE). Subjective QoE assessment involves carefully controlled laboratory environments to generate the desired conditions between a large set of users. The requirements for a cloud-based social XR service lab-setup are complex: Identify a reliable streaming service, a customizable VR application, emulate network conditions, define activities or tasks for users to perform, collect their data, label it; all while mitigating possible human mistakes. To address these requirements, we present an effective technical setup that can consistently repeat the same conditions between users and that can be easily replicated to other labs conducting cloud-based social XR research.

Interactivity is a metric that measures the level of control or manipulation users may exert over a system, software, or service. It is considered a key dimension in cloud-gaming services, measuring how effectively users can control and respond to game events in real-time. Although widely acknowledged by standards such as ITU-T Rec. G.1051, G.1072, its relationship to other quality dimensions such as video, audio, and overall Quality of Experience (QoE), remains not thoroughly examined. In this paper, we present a novel Bayesian Network-based framework to model and analyze interactivity alongside other quality factors under varied network conditions. Our method enables probabilistic inference and sensitivity analysis across perceptual variables, offering explainable insights into their mutual influence. Using data from two (\textit{N1}=30, and \textit{N2}=31 subjects) subjective studies — one for Virtual Reality Cloud Gaming (VRCG) and another for Mobile Cloud Gaming (MCG) — we show interactivity is most sensitive to round trip time (RTT) but resilient to jitter (RJ) effect. Further, an assessment of the seven interactivity-only variables shows that their distributions change uniformly under varying network conditions, suggesting that interactivity may be captured by a single metric. Finally, sensitivity analyses indicate that QoE is a more representative metric than interactivity for quality assessment in cloud gaming over heterogeneous access networks.