URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems

Park, Jihong; Bennis, Mehdi

doi:10.1109/glocom.2018.8647208

Cited by 62 publications

(44 citation statements)

References 24 publications

(54 reference statements)

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“…At the same time, the eMBB traffic should be minimally impacted when maximizing the URLLC outage capacity. A recent work towards this direction can be found in [47] as well as a recent application in the context of virtual reality [48].…”

Section: A Low-latencymentioning

confidence: 99%

Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale

2018

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Ensuring ultra-reliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay and average response time) is no longer an option but a necessity. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture, and topology (across access, edge, and core) and decisionmaking under uncertainty is sorely lacking. The overarching goal of this article is a first step to fill this void.Towards this vision, after providing definitions of latency and reliability, we closely examine various enablers of URLLC and their inherent tradeoffs. Subsequently, we focus our attention on a plethora of techniques and methodologies pertaining to the requirements of ultra-reliable and low-latency communication, as well as their applications through selected use cases. These results provide crisp insights for the design of lowlatency and high-reliable wireless networks. Index TermsUltra-reliable low-latency communication, 5G and beyond, resource optimization, mobile edge computing. I. IntroductionThe phenomenal growth of data traffic spurred by the internet-of-things (IoT) applications ranging from machine-type communications (MTC) to mission-critical communications (autonomous driving, drones and augmented/virtual reality) are posing unprecedented challenges in terms of capacity, latency, reliability, and [4]. This is further exacerbated by: i) a growing network size and increasing interactions between nodes; ii) a high level of uncertainty due to random changes in the topology; and iii) a heterogeneity across applications, networks and devices. The stringent requirements of these new applications warrant a paradigm shift from reactive and centralized networks towards massive, low-latency, ultra-reliable and M. Bennis is with the Centre for wireless communications,

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Section: A Low-latencymentioning

confidence: 99%

Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale

2018

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“…decisions for safe operation [12], [13]. A user enjoying visuo-haptic perceptions requires not only minimal individual perception delays but also minimal delay variance to avoid motion sickness [14], [15]. A remotely controlled drone or a robotic assembler in a smart factory should always be operational even when the network connection is temporarily unavailable [16]- [18], by sensing and reacting rapidly to the local (and possibly hazardous) environments.…”

Section: Fig 1 Illustration Of Edge ML Where Both Ml Inference and mentioning

confidence: 99%

Wireless Network Intelligence at the Edge

et al. 2019

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edge devices. The new breed of intelligent devices and high-stake applications (drones, augmented/virtual reality, autonomous systems, and so on) requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML). In edge ML, training data are unevenly distributed over a large number of edge nodes, which have access to a tiny fraction of the data. Moreover, training and inference are carried out collectively over wireless links, where edge devices communicate and exchange their learned models (not their private data). In a first of its kind, this article explores the key building blocks of edge ML, different neural network architectural splits and their inherent tradeoffs, as well as theoretical and technical enablers stemming from a wide range of mathematical disciplines. Finally, several case studies pertaining to various high-stake applications are presented to demonstrate the effectiveness of edge ML in unlocking the full potential of 5G and beyond.

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“…In the adopted tile-based streaming approach, as per [8]- [11], the video frame is divided in a grid of regular tiles which will each be encoded in both HD and at a lower resolution. Then, based on head 2 tracking information, only the tiles within a user's FoV region will be streamed in HD.…”

Section: A Related Workmentioning

confidence: 99%

“…5. · · · · · · · · · Input: 512 Output: N · · · · · · · · · 0/1 (Tile 2) o (2) f is then fed to a serial to parallel (S/P) layer before going across a dense neural layer that connects with the N output neurons.…”

Section: Drnn Fov Prediction and Fov+location Aware User Clusteringunclassified

“…With the premise that only when both high-quality and low-latency requirements are fulfilled does immersive virtual reality experience hold, high-end VR manufacturers have been long compelled to using wired connections between the head C. Perfecto mounted displays (HMDs) and VR servers with high processing and storage capabilities. However, this constraint physically limits the movement of VR users and hence, degrades the quality-of-experience (QoE), thereby calling for further development of mobile/wireless solutions that, aware of the different multimodal perceptions involved, are able to provide both convenience and a high-quality VR [2]. Moreover, as access to social VR experiences surges, driven by locationbased VR and 360 • formats, the gap between available and mobile immersive VR required prohibitively high bandwidth is likely to keep on growing.…”

Section: Introductionmentioning

confidence: 99%

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Taming the Latency in Multi-User VR 360°: A QoE-Aware Deep Learning-Aided Multicast Framework

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Elbamby

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et al. 2020

IEEE Trans. Commun.

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Immersive virtual reality (VR) applications are known to require ultra-high data rate and low-latency for smooth operation. In this paper, we propose a proactive deep-learning aided joint scheduling and content quality adaptation scheme for multi-user VR field of view (FoV) wireless video streaming. Using a real VR head-tracking dataset, a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) is leveraged to obtain users' upcoming tiled FoV predictions. Subsequently, to exploit a physical layer FoV-centric millimeter wave (mmWave) multicast transmission, users are hierarchically clustered according to their predicted FoV similarity and location. We pose the problem as a quality admission maximization problem under tight latency constraints, and adopt the Lyapunov framework to model the problem of dynamically admitting and scheduling proactive and real-time high definition (HD) video chunk requests corresponding to a tile in the FoV of a cluster user for a given video frame while maintaining the system stability. After decoupling the problem into three subproblems, a matching theory game is proposed to solve the scheduling subproblem by associating chunk requests from clusters of users to mmWave small cell base stations (SBSs) for multicast transmission. Simulation results demonstrate the streaming quality gain and latency reduction brought by using the proposed scheme. It is shown that the prediction of FoV significantly improves the VR streaming experience using proactive scheduling of the video tiles in the users' future FoV. Moreover, multicasting significantly reduces the VR frame delay in a multi-user setting by applying contentreuse in clusters of users with highly overlapping FoVs.

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URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems

Cited by 62 publications

References 24 publications

Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale

Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale

Wireless Network Intelligence at the Edge

Taming the Latency in Multi-User VR 360°: A QoE-Aware Deep Learning-Aided Multicast Framework

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