Virtual Reality Gaming on the Cloud: A Reality Check

Zhao, Sihao; Abou-zeid, Hatem; Atawia, Ramy; Manjunath, Yoga Suhas Kuruba; Sediq, Akram Bin; Zhang, Xiaoping

doi:10.1109/globecom46510.2021.9685808

Cited by 25 publications

(11 citation statements)

References 10 publications

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“…Finally, the proposed hybrid TL-aided DRL approach ensures that the maximum level of rewards is reached safely and quickly. We demonstrate that using real network VR gaming traffic to reflect an important immersive and latency-intolerant scenario in NGNs [24].…”

Section: Related Workmentioning

confidence: 97%

Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing

Nagib

Abou-zeid

Hassanein

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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Deep Reinforcement Learning (DRL) algorithms have been recently proposed to solve dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow convergence experienced by traditional DRL agents puts many doubts on their practical adoption in cellular networks. In this paper, we first discuss the need to have accelerated DRL algorithms. We then analyze the exploration behavior of various state-of-the-art DRL algorithms for slice resource allocation, and compare it with the traditional 5G Radio Access Network (RAN) slicing baselines. Finally, we propose a transfer learningaccelerated DRL-based solution for slice resource allocation. In particular, we tackle the challenge of slow convergence by transferring the policy learned by a DRL agent at an expert base station (BS) to newly deployed agents at target learner BSs. Our approach shows a remarkable reduction in convergence time and a significant performance improvement compared with its nonaccelerated counterparts when tested against multiple traffic load variations.

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Section: Related Workmentioning

confidence: 97%

Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing

Nagib

Abou-zeid

Hassanein

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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“…Overall, 23 pieces of literature were selected to identify issues and challenges related to networking that currently affect cloud gaming. The identified issues and challenges are quality of service (QoS) and quality of experience (QoE) [17]- [21], latency [20], [22]- [30], bandwidth [20], [24], [26], [27], [31]- [33], delay [17], [23], [27], [31], [34]- [39], packet loss [17], [19], [22], [30], the cost of providing the service [31]- [33], complexity (video encoding, compression, and game content) [20], [34], [36], [38], and graphics quality [23], [34]- [36]. Figure 2 presents these issues and challenges along with the number of corresponding literature mainly cited to discuss them.…”

Section: The Issues and Challenges Related To Networking That Current...mentioning

confidence: 99%

“…Such a problem is applicable to various types of games. For instance, VR games in a cloud gaming setting may not be as useful if the delay is high [21]. The other pieces of collected literature, references [21]- [35], also correlate QoE with other factors.…”

Section: Quality Of Service (Qos) and Quality Of Experience (Qoe)mentioning

confidence: 99%

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An Overview of the Networking Issues of Cloud Gaming

Baldovino

2022

jinita

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With the increasing prevalence of video games comes innovations that aim to evolve them. Cloud gaming is poised as the next phase of gaming. It enables users to play video games on any internet-enabled device. Such improvement could, therefore, enhance the processing power of existing devices and solve the need to spend large amounts of money on the latest gaming equipment. However, others argue that it may be far from being practically functional. Since cloud gaming places dependency on networks, new issues emerge. In relation, this paper is a review of the networking perspective of cloud gaming. Specifically, the paper analyzes its issues and challenges along with possible solutions. In order to accomplish the study, a literature review was performed. Results show that there are numerous issues and challenges regarding cloud gaming networks. Generally, cloud gaming has problems with its network quality of service (QoS) and quality of experience (QoE). The poor QoS and QoE of cloud gaming can be linked to unsatisfactory latency, bandwidth, delay, packet loss, and graphics quality. Moreover, the cost of providing the service and the complexity of implementing cloud gaming were considered challenges. For these issues and challenges, solutions were found. The solutions include lag or latency compensation, compression with encoding techniques, client computing power, edge computing, machine learning, frame adaption, and GPU-based server selection. However, these have limitations and may not always be applicable. Thus, even if solutions exist, it would be beneficial to analyze the networking side of cloud gaming further.

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“…Lastly, we would like to mention a study on traffic modeling. The authors in [12] considered both local and cloud rendering and proposed a model for the video frame size and one for the interarrival time.…”

Section: Introductionmentioning

confidence: 99%

Is Wi-Fi 6 Ready for Virtual Reality Mayhem? A Case Study Using One AP and Three HMDs

Michaelides,

Casasnovas,

Marchitelli

et al. 2023

2023 IEEE Future Networks World Forum (FNWF)

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Is Wi-Fi 6 ready for virtual reality (VR) mayhem? The common use case for untethered VR is running a video game on a powerful desktop and streaming the generated content to a head-mounted display (HMD) over Wi-Fi. In this work, we investigated a challenging use case using one Wi-Fi 6 access point (AP) and three HMDs. Specifically, we evaluated experimentally the performance of VR streaming in terms of latency using: i) the distributed coordination function (DCF), ii) orthogonal frequency-division multiple access (OFDMA) in the downlink (DL) and the uplink (UL) and iii) multi-user multiple input multiple output (MU-MIMO). We showed that VR streaming to three users, up to 100 Mbps each, works flawlessly using either DCF or DL OFDMA, with latency in the order of 50 ms. In addition, our study unveiled the disruption of tracking data transmissions in the uplink using UL OFDMA, which had a negative impact on latency by delaying game rendering.

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Virtual Reality Gaming on the Cloud: A Reality Check

Cited by 25 publications

References 10 publications

Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing

Transfer Learning-Based Accelerated Deep Reinforcement Learning for 5G RAN Slicing

An Overview of the Networking Issues of Cloud Gaming

Is Wi-Fi 6 Ready for Virtual Reality Mayhem? A Case Study Using One AP and Three HMDs

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