Abstract-Virtual Reality (VR) is expected to be one of the killerapplications in 5G networks. However, many technical bottlenecks and challenges need to be overcome to facilitate its wide adoption. In particular, VR requirements in terms of high-throughput, low-latency and reliable communication call for innovative solutions and fundamental research cutting across several disciplines. In view of this, this article discusses the challenges and enablers for ultra-reliable and low-latency VR. Furthermore, in an interactive VR gaming arcade case study, we show that a smart network design that leverages the use of mmWave communication, edge computing and proactive caching can achieve the future vision of VR over wireless.

In this paper, the problem of content-aware user clustering and content caching in wireless small cell networks is studied. In particular, a service delay minimization problem is formulated, aiming at optimally caching contents at the small cell base stations (SCBSs). To solve the optimization problem, we decouple it into two interrelated subproblems. First, a clustering algorithm is proposed grouping users with similar content popularity to associate similar users to the same SCBS, when possible. Second, a reinforcement learning algorithm is proposed to enable each SCBS to learn the popularity distribution of contents requested by its group of users and optimize its caching strategy accordingly. Simulation results show that by correlating the different popularity patterns of different users, the proposed scheme is able to minimize the service delay by 42% and 27%, while achieving a higher offloading gain of up to 280% and 90%, respectively, compared to random caching and unclustered learning schemes.

Abstract-In this paper, the fundamental problem of distribution and proactive caching of computing tasks in fog networks is studied under latency and reliability constraints. In the proposed scenario, computing can be executed either locally at the user device or offloaded to an edge cloudlet. Moreover, cloudlets exploit both their computing and storage capabilities by proactively caching popular task computation results to minimize computing latency. To this end, a clustering method to group spatially proximate user devices with mutual task popularity interests and their serving cloudlets is proposed. Then, cloudlets can proactively cache the popular tasks' computations of their cluster members to minimize computing latency. Additionally, the problem of distributing tasks to cloudlets is formulated as a matching game in which a cost function of computing delay is minimized under latency and reliability constraints. Simulation results show that the proposed scheme guarantees reliable computations with bounded latency and achieves up to 91% decrease in computing latency as compared to baseline schemes.

In this paper, a novel proactive computing and mmWave communication for ultra-reliable and low latency wireless virtual reality (VR is proposed. By leveraging information about users' poses, proactive computing and caching are used to pre-compute and store users' HD video frames to minimize the computing latency. Furthermore, multi-connectivity is exploited to ensure reliable mmWave links to deliver users' requested HD frames. The performance of the proposed approach is validated on a VR network serving an interactive gaming arcade, where dynamic and real-time rendering of HD video frames is needed and impulse actions of different players impact the content to be shown. Simulation results show significant gains of up to 30% reduction in end-to-end delay and 50% in the 90 th percentile communication delay.

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.

Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and lowlatency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.

Abstract-In this paper, the problem of uplink (UL) and downlink (DL) resource optimization, mode selection and power allocation is studied for wireless cellular networks under the assumption of in-band full duplex (IBFD) base stations, nonorthogonal multiple access (NOMA) operation, and queue stability constraints. The problem is formulated as a network utility maximization problem for which a Lyapunov framework is used to decompose it into two disjoint subproblems of auxiliary variable selection and rate maximization. The latter is further decoupled into a user association and mode selection (UAMS) problem and a UL/DL power optimization (UDPO) problem that are solved concurrently. The UAMS problem is modeled as a many-to-one matching problem to associate users to small cell base stations (SBSs) and select transmission mode (half/fullduplex and orthogonal/non-orthogonal multiple access), and an algorithm is proposed to solve the problem converging to a pairwise stable matching. Subsequently, the UDPO problem is formulated as a sequence of convex problems and is solved using the concave-convex procedure. Simulation results demonstrate the effectiveness of the proposed scheme to allocate UL and DL power levels after dynamically selecting the operating mode and the served users, under different traffic intensity conditions, network density, and self-interference cancellation capability. The proposed scheme is shown to achieve up to 63% and 73% of gains in UL and DL packet throughput, and 21% and 17% in UL and DL cell edge throughput, respectively, compared to existing baseline schemes.

This paper studies the problem of task distribution and proactive edge caching in fog networks with latency and reliability constraints. In the proposed approach, user nodes (UNs) offload their computing tasks to edge computing servers (cloudlets). Cloudlets leverage their computing and storage capabilities to proactively compute and store cacheable computing results. In this regard, a task popularity estimation and caching policy schemes are proposed. Furthermore, the problem of UNs' tasks distribution to cloudlets is modeled as a one-to-one matching game. In this game, UNs whose requests exceed a delay threshold use the notion of hedged-requests to enqueue their request in another cloudlet, and offload the task data to whichever is available first. A matching algorithm based on the deferred-acceptance matching is used to solve this game. Simulation results show that the proposed approach guarantees reliable service and minimal latency, reaching up to 50 and 65% reduction in the average delay and the 99th percentile delay, as compared to reactive baseline schemes.