While Poisson Cluster Process (PCP) has been used extensively in the literature for analyzing large-scale clustered wireless networks, related work differs in terms of how the BS (Base Station) distribution and UE (User Equipment) association strategy are modeled in this context. Different from related work that places BSs via Poisson Point Process (PPP) independent of the UE distribution, we consider a more practical scenario where the BSs are located at the parent points of PCP. Furthermore, while a lot of analysis often assumes each UE to be associated with the BS at the parent point of the cluster it belongs to, such a model may result in bias for dense networks where clusters of UEs may overlap with each other. To address such a pitfall, in this work we consider “nearest-BS” association such that a UE may be associated with a nearby BS that is the parent point of another cluster. While there does exist analysis on downlink transmissions for such a network scenario, we find that analysis for uplink performance is more challenging and has yet to be developed in the literature. To proceed, we start from the analytical framework established by related work but derive new expressions for the aggregate uplink interference from different UE clusters in the network. We derive accurate analytical upper and lower bounds for the transmission success probability and we are able to arrive at a closed-form expression for the special case with a path loss exponent of 4. Furthermore, we derive the asymptotic performance bound for ultra-dense networks and investigate the problem of optimizing the area spectral efficiency (ASE) with respect to the design of the signal-to-interference-plus-noise ratio (SINR) threshold. By leveraging the closed-form expression that we have derived, the problem can be solved effectively to obtain the optimal ASE in ultra-dense networks. Evaluation results show that our analysis is correct and the derived bounds are accurate, thus motivating further investigation based on the derived results.

Current wireless volumetric video streaming faces multiple challenges, including high bandwidth consumption, large decoding overhead on the client device, and low view reconstruction quality perceived by the user. In this paper, we explore an edge-assisted streaming system, where 3D contents of volumetric video are first transcoded by the edge server based on predicted user positions and then transmitted to the client to substantially reduce the bandwidth consumption and decoding overhead. In such a system, however, inaccuracy in position prediction could lead to displacement between the transcoded reference view and the actual view required by the client. To mitigate this problem, we task the edge server to transcode multiple views and formulate the problem of multiview generation as an optimal quantization problem. Rather than using distance-based distortion metrics, our formulation considers the actual distortion perceived by the user for solving the general problem of positioning reference views under general position error distribution caused by position predictors. Our evaluations show significant improvements over state-of-the-art techniques by reducing quality degradation from 17% to 28% in terms of the 75-th and 95-th percentiles of distortion perceived by users.

Wireless virtual reality (VR), aiming to provide an untethered immersive experience through 360° videos, could be facilitated by viewport-guided streaming with the help of viewport prediction. Although many recent viewport predictors can output a series of predictions over upcoming frames, most existing work on video streaming does not fully utilize the capability of these predictors. In this paper, we investigate the problem of 360° video streaming by incorporating the complete series of viewport predictions for maximizing the quality of experience (QoE) through cross-frame resource allocation. To address the problem of viewport prediction errors that could result in erroneous estimation of QoE contribution of tiles in upcoming frames, we develop a novel approach based on contextual multi-armed bandit (CMAB) to “learn” online the viewing behavior of the user and the capability of the predictor such that resource can be preferentially allocated to tiles with significant QoE contribution. Further, to address the problem of transmission failures during wireless streaming, we formulate a constrained Markov decision process (CMDP) and apply model predictive control (MPC) to account for resource competition among reactive and proactive transmissions as well as retransmissions of tiles. The performance of the proposed streaming system is evaluated using a real-world VR dataset, state-of-the-art viewport predictors, and realistic mmWave channel models. An improvement of 5.68% in QoE and a reduction of 23.0% in resource waste are achieved across various videos, users, and predictors. Simulation results substantiate that the context-aware QoE learned by the proposed CMAB effectively addresses prediction errors for tiles with different temporal and spatial contexts, and the proposed CMDP can achieve the desired performance even under high viewport prediction error and channel error.