Analysis and QoE evaluation of cloud gaming service adaptation under different network conditions: The case of NVIDIA GeForce NOW

Sužnjević, Mirko; Slivar, Ivan; Skorin‐Kapov, Lea

doi:10.1109/qomex.2016.7498968

Cited by 33 publications

(20 citation statements)

References 9 publications

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“…The server receives the commands, updates the scene accordingly, and transmits the next frames to the headset. Such a workflow is typical for the existing cloud-based gaming and XR systems [10], [33], [34].…”

Section: )mentioning

confidence: 99%

Adaptive Cloud-Based Extended Reality: Modeling and Optimization

et al. 2021

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Extended Reality (XR)-which includes Virtual Reality and Augmented Reality-promises to bring the virtual and telepresence experience to another level. Unfortunately, solutions leveraging these technologies require special high-performance computing platforms that degrade the cost-benefit balance. Moving processing to the cloud solves this problem but imposes strict requirements on data transmission reliability, bandwidth, and delays. The satisfaction of these requirements becomes an extremely challenging problem in the presence of other types of delay-sensitive traffic, such as remote control, industrial automation, or the control commands of the Cloud XR application itself. This article studies the joint service of the adaptive Cloud XR traffic with other high-priority delay-sensitive traffics. The paper develops an analytical model of the considered communication system. The model represents the system as a discrete state Markov chain and estimates the quality of experience for Cloud XR users in various scenarios. Using the model, the paper estimates the network capacity for the Cloud XR traffic and optimizes the bitrate adaptation function of the Cloud XR video streaming application. The goal of the optimization is to improve the visual quality of the virtual environment observed by the users, subject to the constrained probability of image impairments due to excessive delivery delays. Numerical results demonstrate the high accuracy of the developed model and the benefits provided by the optimization. INDEX TERMS Cloud XR, heterogeneous traffic, high-priority traffic, real-time adaptive video, virtual reality, quality of experience, queueing systems, analytical models, Markov chain.

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Section: )mentioning

confidence: 99%

Adaptive Cloud-Based Extended Reality: Modeling and Optimization

et al. 2021

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“…The proposed mechanism adjusts the game parameters in a real time manner to improve the entertainment for the player. A commercial analysis of NVIDIA GeForce NOW game streaming is conducted in [17]. The analysis highlights the problems of network limitations and proposed an adaption mechanism of game streaming considering the end user network constraints.…”

Section: Previous Workmentioning

confidence: 99%

Efficient Cloud Gaming Scheme Using Scene Objects Adaptation

Mazhar¹

2017

IJCSIT

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“…With very limited literature on the subject, the field and the tools employed for QoE evaluation of cloud gaming applications are far from maturity. There are accounts of subjective testing being used to evaluate the effect of cloud gaming influencing factors, such as video encoder, frame rate and QoS parameters [4]. However, as game development is becoming more J 2021, 4 sophisticated, it is vital to keep up the QoE evaluation targeted for more complex and high-end games.…”

Section: Introductionmentioning

confidence: 99%

“…Our results add significant value to existing subjective QoE studies by proving the difference between video quality and game playability quality using distributions and statistical tests. For instance, in [4,7], the authors used MOS to evaluate the effect of network parameters on QoE of cloud gaming. These studies used MOS as a measure of QoE and presented the differences between game QoE and video QoE of cloud gaming applications.…”

Section: Introductionmentioning

confidence: 99%

Subjective Quality Assessment for Cloud Gaming

Wahab

Ahmad

Martini

et al. 2021

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Using subjective testing, we study the effect of the network parameters, delay and packet loss ratio, on the QoE of cloud gaming. We studied three different games, selected based on genre, popularity, content complexity and pace, and tested them in a controlled network environment, using a novel emulator to create realistic lognormal delay distributions instead of relying on a static mean delay, as used previously; we also used Parsec as a good representative of the state of the art. We captured user ratings on an ordinal Absolute Category Rating scale for three quality dimensions: Video QoE, Game-Playability QoE, and Overall QoE. We show that Mean Opinion Scores (MOS) for the game with the highest levels of content complexity and pace are most severely affected by network impairments. We also show that the QoE of interactive cloud applications rely more on the game playability than the video quality of the game. Unlike earlier studies, the differences in MOS are validated using the distributions of the underlying dimensions. A Wilcoxon Signed-Rank test showed that the distributions of Video QoE and Game Playability QoE are not significantly different.

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Analysis and QoE evaluation of cloud gaming service adaptation under different network conditions: The case of NVIDIA GeForce NOW

Cited by 33 publications

References 9 publications

Adaptive Cloud-Based Extended Reality: Modeling and Optimization

Adaptive Cloud-Based Extended Reality: Modeling and Optimization

Efficient Cloud Gaming Scheme Using Scene Objects Adaptation

Subjective Quality Assessment for Cloud Gaming

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