Facilitating Low Latency and Reliable VR over Heterogeneous Wireless Networks

Ravichandran, Arunkumar; Jain, Ish Kumar; Hegazy, Rana D.; Wei, Teng; Bharadia, Dinesh

doi:10.1145/3241539.3267781

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…For example, in mapping the E2E latency between the application layers of two devices, the authors of [18] established that 10-15 ms time delays are typically encountered in indoor scenarios with a direct line-of-sight between the two communicating devices. The results obtained using the ns-3 network simulator are roughly similar to the experimental findings in [19][20][21][22]. All of these studies elaborate on the appropriateness of WiGig for serving XR applications, receiving high-resolution video streams over the network.…”

Section: Related Work On Latency Reductionsupporting

confidence: 75%

“…All of these studies elaborate on the appropriateness of WiGig for serving XR applications, receiving high-resolution video streams over the network. Their approach to latency reduction includes combining WiGig with sub-6 GHz Wi-Fi [22], dynamically tuning the video encoder [20], synchronising data transmission with application-specific events [21], and leveraging user pose information for quicker beam-and access point (AP) switching [19]. As a result of these latency mitigation strategies, the range of expected latency values of individual video frames gets extended to 5-50 ms.…”

Section: Related Work On Latency Reductionmentioning

confidence: 99%

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Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi

et al. 2021

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Emerging applications in fields such as extended reality require both a high throughput and low latency. The millimeter-wave (mmWave) spectrum is considered because of the potential in the large available bandwidth. The present work studies mmWave Wi-Fi physical layer latency management mechanisms, a key factor in providing low-latency communications for time-critical applications. We calculate physical layer latency in an ideal scenario and simulate it using a tailor-made simulation framework, based on the IEEE 802.11ad standard. Assessing data reception quality over a noisy channel yielded latency’s dependency on transmission parameters, channel noise, and digital baseband tuning. Latency in function of the modulation and coding scheme was found to span 0.28–2.71 ms in the ideal scenario, whereas simulation results also revealed its tight bond with the demapping algorithm and the number of low-density parity-check decoder iterations. The findings yielded tuning parameter combinations for reaching Pareto optimality either by constraining the bit error rate and optimizing latency or the other way around. Our assessment shows that trade-offs can and have to be made to provide sufficiently reliable low-latency communication. In good channel conditions, one may benefit from both the very high throughput and low latency; yet, in more adverse situations, lower modulation orders and additional coding overhead are a necessity.

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Section: Related Work On Latency Reductionsupporting

confidence: 75%

Section: Related Work On Latency Reductionmentioning

confidence: 99%

Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi

et al. 2021

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“…However, the large encoding and decoding time before playback is not considered in their research. Arunkumar, et al study the relationship between the decoding time and transmission time in VR video and propose to balance the relationship between the two by partially transferring raw tiles of VR video [14]. However, this research does not optimize this process and can not be directly used in point cloud video streaming due to the unique features of point cloud video.…”

Section: Introductionmentioning

confidence: 99%

Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming

Zhang

Liu

et al. 2020

Preprint

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Point cloud video is the most popular representation of hologram, which is the medium to precedent natural content in VR/AR/MR and is expected to be the next generation video. Point cloud video system provides users immersive viewing experience with six degrees of freedom (6DoF) and has wide applications in many fields such as online education and entertainment. To further enhance these applications, point cloud video streaming is in critical demand. The inherent challenges lie in the large size by the necessity of recording the three-dimensional coordinates besides color information, and the associated high computation complexity of encoding/decoding. To this end, this paper proposes a communication and computational resource allocation scheme for QoE-driven point cloud video streaming. In particular, with the goal to maximize the defined QoE by selecting proper quality levels (uncompressed tiles at different quality levels are also considered) for each partitioned point cloud video tile, we formulate this into an optimization problem under the limited communication and computational resources constraints and propose a scheme to solve it. Extensive simulations are conducted and the simulation results show the superior performance of the proposed scheme over the existing schemes.

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Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming

Zhang

Liu

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Facilitating Low Latency and Reliable VR over Heterogeneous Wireless Networks

Cited by 5 publications

References 4 publications

Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi

Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi

Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming

Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming

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