How to measure and model QoE for networked games?

Sužnjević, Mirko; Skorin‐Kapov, Lea; Cerekovic, Aleksandra; Matijašević, Maja

doi:10.1007/s00530-019-00615-x

Cited by 16 publications

(9 citation statements)

References 40 publications

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“…However, they did not mention about QoE modeling based on MOBA games. From other previous research works, there are several interesting works associated with QoE models, including objective-online game QoE models [ 37 , 39 , 42 , 47 ], but only the subjective QoE models are presented in Table 2 that can be briefly described as follows: Dwyer & Finn [ 15 ] performed a study to assess MOS of gameplay under the controlled conditions based on latency. They recruited 32 volunteers to play the game ‘Team Fortress 2’, before the analysis and modeling two MOS models associated with delay and jitter as shown in (1)–(2).…”

Section: Background and Related Workmentioning

confidence: 99%

QoE modeling for audiovisual associated with MOBA game using subjective approach

Pornpongtechavanicha

Wuttidittachotti

Daengsi

2022

Multimed Tools Appl

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With the advancement of multimedia communications and information technologies, online video games have become very popular recreational activities worldwide, while Multiplayer Online Battle Arena (MOBA) games have gained immense popularity from game players in recent times. Therefore, based on quality of experience, audiovisual quality evaluation models for MOBA games have been proposed in this study. The subjective data from 210 game players (both males and females) about audio quality during conversation, the video quality, the overall quality, and the weighted percentage of video and voice have been collected from small competition events of the Garena ROV (Arena of Valor) in Hua Hin, Thailand. The data from 160 game players have been used to create the models, while the data from 50 game players have been applied for model performance evaluation. After developing the models, Mean Absolute Percentage Error (MAPE) technique has been utilized for model evaluation. On completion of this study, it was found that two thirds of the proposed models provide better performance than the other previous works. All of them, called MOS Audiovisual-MOBA1 model, MOS Audiovisual-MOBA2 model and MOS Audiovisual-MOBA3 model, show excellent performances with MAPE values of 4.95%, 5.92% and 4.75% respectively. Therefore, excellent performance of these proposed models becomes one of the contributions of this study. Also, this evidence of excellent model performance makes the proposed models ideal for utilization for assessment of audiovisual quality of the MOBA games. All audiovisual quality evaluation models provide excellent efficiencies.

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Section: Background and Related Workmentioning

confidence: 99%

QoE modeling for audiovisual associated with MOBA game using subjective approach

Pornpongtechavanicha

Wuttidittachotti

Daengsi

2022

Multimed Tools Appl

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“…Jitter does not follow a uniformly random distribution across real-world networks and can be largely masked by packet reordering schemes; Qin et al showed that packet loss that fails to model burst loss might not be perceivable by users 26 . In our studies, we evaluate the effect of these “packet bursts” (also known as jerkiness 27 ). Our goal in evaluating packet bursts is to measure the effect of “lag,” a loosely defined term for the responsiveness of an application often used by gamers to quantify the QoS 28 .…”

Section: Methodsmentioning

confidence: 99%

“…We found through preliminary testing that uniformly distributed jitter poorly emulated real-world lag, particularly with packet order enforcement. Suzejevic et al incorporate a QoS measure of “jerkiness” which also includes a periodic slowdown by overloading the system with randomly spawned processes to freeze the application 27 . We take a more systematic approach by introducing this freezing mechanism at the network level, and we determined parameters for packet bursts that appeared to best emulate lag through preliminary testing.…”

Section: Methodsmentioning

confidence: 99%

Disruptions to shared mental models from poor quality of service in collaborative virtual environments

Milef

Ryason

et al. 2021

Sci Rep

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Collaborative virtual environments are being used in various applications ranging from online games to complex team training scenarios. The key to the success of such environments is the ability of the participants to form a shared mental model of the collaborative task being performed. Poor quality of service can deteriorate user performance and quality of experience, leading to a disruption of this mental model. While the effects of quality of service have been analyzed for traditional desktop environments, these effects remain unclear in collaborative virtual environments during user-to-user interactions. Here, we analyze the role of latency and packet bursts, two common problems in collaborative applications, on both simulator perception and actual task performance in a collaborative fire-fighting simulator. This exploratory study indicates that large latencies have a significant (p < 0.05) impact on the quality of experience, but not task performance. In contrast, packet bursts have a much larger impact on both the quality of experience and performance. Additionally, the network role, such as whether a user is a client or server, showed a significant (p < 0.05) impact on task performance in conditions impaired by packet bursts.

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“…The model for QoE prediction centering on MMORPGs [176], examines system, user, and context elements and assesses their influence on QoE. It also addresses certain methodological issues linked to assessing gaming QoE and delves deeper into a collection of quality metrics beyond MOS, like the percentages of users rating the gameplay scenario, and acceptance measures.…”

Section: Video Gamingmentioning

confidence: 99%

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

et al. 2022

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The groundbreaking evolution in mobile and wireless communication networks design in recent years, in combination with the advancement of mobile terminal equipment capabilities, has led in an exponential growth of mobile internet technologies, and arose an ever-growing demand for innovative multimedia services. The highly demanding in terms of network resources over-the-top media services, as well as the emergence of new and complex mobile multimedia services such as video gaming, ultra-highdefinition video, and extended reality, requires the enhancement of end-users' perceived quality of experience (QoE). QoE has garnered much research interest in recent years, and has emerged as a key component in the evaluation of network services and operations. As a result, a QoE-aware network planning approach is getting increasingly favored, and novel design challenges, such as how to quantify and measure QoE, have arisen. In this regard, a paradigm shift in network implementations is being envisioned, in which the focus will be on machine learning (ML) methodologies for developing QoE prediction models, directly related to end-user's personalized experience. In this survey, an analysis on application-oriented, ML-based QoE prediction models for the goal of QoE management for multimedia services is presented. In addition, an examination of the state-of-the-art ML-based QoE predictive models and some of the innovative techniques and challenges related to multimedia services quality assessment with focus on extended reality and video gaming applications are outlined.

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How to measure and model QoE for networked games?

Cited by 16 publications

References 40 publications

QoE modeling for audiovisual associated with MOBA game using subjective approach

QoE modeling for audiovisual associated with MOBA game using subjective approach

Disruptions to shared mental models from poor quality of service in collaborative virtual environments

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

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