Measuring Key Quality Indicators in Cloud Gaming: Framework and Assessment Over Wireless Networks

Penaherrera-Pulla, O. S.; Baena, Carlos; Fortes, Sergio; Baena, Eduardo; Barco, Raquel

doi:10.3390/s21041387

Cited by 39 publications

(16 citation statements)

References 31 publications

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“…A more comprehensive Cloud gaming testbed, including a full implementation with different network alternatives and automated trace acquisition over Ethernet, WiFi, and LTE, was presented in [20]. This is surely an advantage in terms of reproducibility and speed of the experiments, but the unpredictability of the users' actions in gaming scenarios (and, more importantly, in XR) is the real challenge that the network has to face, limiting the usefulness of the results.…”

Section: State Of the Artmentioning

confidence: 99%

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Lecci¹,

Chiariotti²,

Drago³

et al. 2022

Preprint

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Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR's interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.

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Section: State Of the Artmentioning

confidence: 99%

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Lecci¹,

Chiariotti²,

Drago³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A more comprehensive Cloud gaming testbed, including automated trace acquisition over Ethernet, WiFi, and LTE, was presented in [27]. Automating the acquisitions surely gives an advantage in terms of reproducibility and speed of the experiments, but the unpredictability of the users' actions in gaming scenarios (and, more importantly, in XR) is the real challenge that the network has to face, limiting the usefulness of the results.…”

Section: Xr Traffic Characterizationmentioning

confidence: 99%

Temporal Characterization of VR Traffic for Network Slicing Requirement Definition

Chiariotti¹,

Drago²,

Testolina³

et al. 2022

Preprint

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Over the past few years, the concept of VR has attracted increasing interest thanks to its extensive industrial and commercial applications. Currently, the 3D models of the virtual scenes are generally stored in the VR visor itself, which operates as a standalone device. However, applications that entail multi-party interactions will likely require the scene to be processed by an external server and then streamed to the visors. However, the stringent Quality of Service (QoS) constraints imposed by VR's interactive nature require Network Slicing (NS) solutions, for which profiling the traffic generated by the VR application is crucial. To this end, we collected more than 4 hours of traces in a real setup and analyzed their temporal correlation. More specifically, we focused on the CBR encoding mode, which should generate more predictable traffic streams. From the collected data, we then distilled two prediction models for future frame size, which can be instrumental in the design of dynamic resource allocation algorithms. Our results show that even the state-of-the-art H.264 CBR mode can have significant fluctuations, which can impact the NS optimization. We then exploited the proposed models to dynamically determine the Service Level Agreement (SLA) parameters in an NS scenario, providing service with the required QoS while minimizing resource usage.

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“…Furthermore, authors in [16] have presented some Key Quality Indicators (KQIs), such as frame rate and frame rendering time Round Trip Time (RTT), for objective QoE evaluation of cloud gaming. They studied the effect of the type of transport network on the presented KQIs.…”

Section: Related Workmentioning

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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Measuring Key Quality Indicators in Cloud Gaming: Framework and Assessment Over Wireless Networks

Cited by 39 publications

References 31 publications

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Temporal Characterization of VR Traffic for Network Slicing Requirement Definition

Subjective Quality Assessment for Cloud Gaming

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