Cloud gaming (CG) services are a key enabler of more accessible and affordable gaming experiences for users worldwide. However, to deliver these services with sufficient Quality of Experience (QoE) over heterogeneous access networks, stakeholders, including network and cloud service providers, and game developers, must first understand their specific performance requirements. For that, this study investigates QoE of PC-based CG under controlled and repeatable network conditions, emulating a range of impairments including round-trip time (RTT), packet loss (PL), random jitter (RJ), bursty jitter (BJ), and bitrate. Two representative games, Terraria and Counter-Strike 2, were tested across 15 network conditions, streamed using the Moonlight/Sunshine platform. Thirty participants rated their QoE, with orchestration and data collection managed by the AL-TRUIST tool. Results show that low bitrate (0.5Mb/s), high RTT (402ms), bursty jitter (702ms), and high RJ (std=3) significantly degrade QoE, especially in QoS-sensitive games. Additionally, we evaluate mobile-CG trained QoE models on PC-CG data and observe significant prediction errors, highlighting the importance of accounting for differences in streaming platform and context when estimating QoE. These findings provide valuable insights for stakeholders optimizing PC-CG over HANs.

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.

The emergence of novel cellular network technologies, within 5G, are envisioned as key enablers of a new set of use-cases, including industrial automation, intelligent transportation, and tactile internet. The critical nature of the traffic requirements ranges from ultra-reliable communications, massive connectivity, and enhanced mobile broadband. Thus, the growing research on cellular network monitoring and prediction aims for ensuring a satisfied user-base and fulfillment of service level agreements. The scope of this study is to develop an approach for predicting the cellular link throughput of end-users, with a goal to benchmark the performance of network slices. First, we report and analyze a measurement study involving real-life cases, such as driving in urban, sub-urban, and rural areas, as well as tests in large crowded areas. Second, we develop machine learning models using lower-layer metrics, describing the radio environment, to predict the available throughput. The models are initially validated on the LTE network and then applied to a non-standalone 5G network. Finally, we suggest scaling the proposed model into the future standalone 5G network. We have achieved 93% and 84% R^2 accuracy, with 0.06 and 0.17 mean squared error, in predicting the end-user's throughput in LTE and non-standalone 5G network, respectively.

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.