Optimal Multi-Quality Multicast for 360 Virtual Reality Video

A 360 virtual reality (VR) video, recording a scene of interest in every direction, provides VR users with immersive viewing experience. However, transmission of a 360 VR video which is of a much larger size than a traditional video to mobile users brings a heavy burden to a wireless network. In this paper, we consider multi-quality multicast of a 360 VR video from a single server to multiple users using time division multiple access (TDMA). To improve transmission efficiency, tiling is adopted, and each tile is pre-encoded into multiple representations with different qualities. We optimize the quality level selection, transmission time allocation and transmission power allocation to maximize the total utility of all users under the transmission time and power allocation constraints as well as the quality smoothness constraints for mixed-quality tiles. The problem is a challenging mixed discrete-continuous opti-mization problem. We propose two low-complexity algorithms to obtain two suboptimal solutions, using continuous relaxation and DC programming, respectively. Finally, numerical results demonstrate the advantage of the proposed solutions.Comment: 7 pages, 7 figures, to be published in IEEE GLOBECOM 201

Optimal Wireless Streaming of Multi-Quality 360 VR Video by Exploiting Natural, Relative Smoothness-enabled and Transcoding-enabled Multicast Opportunities

In this paper, we would like to investigate optimal wireless streaming of a multi-quality tiled 360 virtual reality (VR) video from a server to multiple users. To this end, we propose to maximally exploit potential multicast opportunities by effectively utilizing characteristics of multi-quality tiled 360 VR videos and computation resources at the users' side. In particular, we consider two requirements for quality variation in one field-of-view (FoV), i.e., the absolute smoothness requirement and the relative smoothness requirement, and two video playback modes, i.e., the direct-playback mode (without user transcoding) and transcode-playback mode (with user transcoding). Besides natural multicast opportunities, we introduce two new types of multicast opportunities, namely, relative smoothness-enabled multicast opportunities, which allow flexible tradeoff between viewing quality and communications resource consumption, and transcoding-enabled multicast opportunities, which allow flexible tradeoff between computation and communications resource consumptions. Then, we establish a novel mathematical model that reflects the impacts of natural, relative smoothness-enabled and transcoding-enabled multicast opportunities on the average transmission energy and transcoding energy. Based on this model, we optimize the transmission resource allocation, playback quality level selection and transmission quality level selection to minimize the energy consumption in the four cases with different requirements for quality variation and video playback modes. By comparing the optimal values in the four cases, we prove that the energy consumption reduces when more multicast opportunities can be utilized. Finally, numerical results show substantial gains of the proposed solutions over existing schemes, and demonstrate the importance of effective exploitation of the three types of multicast opportunities.Comment: 14 pages, 5 figures, major revision, IEEE Transations on Multimedia. arXiv admin note: substantial text overlap with arXiv:2001.0190

Optimal Multicast of Tiled 360 VR Video in OFDMA Systems

In this letter, we study optimal multicast of tiled 360 virtual reality (VR) video from one server (base station or access point) to multiple users in an orthogonal frequency division multiple access (OFDMA) system. For given video quality, we optimize the subcarrier, transmission power and transmission rate allocation to minimize the total transmission power. For given transmission power budget, we optimize the subcarrier, transmission power and transmission rate allocation to maximize the received video quality. These two optimization problems are non-convex problems. We obtain a globally optimal closed-form solution and a near optimal solution of the two problems, separately, both revealing important design insights for multicast of tiled 360 VR video in OFDMA systems.Comment: 4 pages, 3 figures, to be published in IEEE Communications Letters. arXiv admin note: text overlap with arXiv:1809.0876

Optimal Multicast of Tiled 360 VR Video

In this letter, we study optimal multicast of tiled 360 virtual reality (VR) video from one server (base station or access point) to multiple users. We consider random viewing directions and random channel conditions, and adopt time division multiple access (TDMA). For given video quality, we optimize the transmission time and power allocation to minimize the average transmission energy. For given transmission energy budget, we optimize the transmission time and power allocation as well as the encoding rate of each tile to maximize the received video quality. These two optimization problems are challenging non-convex problems. We obtain globally optimal closed-form solutions of the two non-convex problems, which reveal important design insights for multicast of tiled 360 VR video. Finally, numerical results demonstrate the advantage of the proposed solutions.Comment: 4 pages, 3 figures, to be published in IEEE Wireless Communications Letter

Taming the latency in multi-user VR 360∘^\circ: A QoE-aware deep learning-aided multicast framework

Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations of viewers watching 360$^\circ$ HD VR videos are capitalized on to realize a proactive FoV-centric millimeter wave (mmWave) physical-layer multicast transmission. The problem is cast as a frame quality maximization problem subject to tight latency constraints and network stability. The problem is then decoupled into an HD frame request admission and scheduling subproblems and a matching theory game is formulated to solve the scheduling subproblem by associating requests from clusters of users to mmWave small cell base stations (SBSs) for their unicast/multicast transmission. Furthermore, for realistic modeling and simulation purposes, a real VR head-tracking dataset and a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) are leveraged. Extensive simulation results show how the content-reuse for clusters of users with highly overlapping FoVs brought in by multicasting reduces the VR frame delay in 12\%. This reduction is further boosted by proactiveness that cuts by half the average delays of both reactive unicast and multicast baselines while preserving HD delivery rates above 98\%. Finally, enforcing tight latency bounds shortens the delay-tail as evinced by 13\% lower delays in the 99th percentile.Comment: Accepted for publication in IEEE Transactions on Communications 17 pages, 10 Figure

Learning-based Prediction, Rendering and Association Optimization for MEC-enabled Wireless Virtual Reality (VR) Network

Wireless-connected Virtual Reality (VR) provides immersive experience for VR users from any-where at anytime. However, providing wireless VR users with seamless connectivity and real-time VR video with high quality is challenging due to its requirements in high Quality of Experience (QoE) and low VR interaction latency under limited computation capability of VR device. To address these issues,we propose a MEC-enabled wireless VR network, where the field of view (FoV) of each VR user can be real-time predicted using Recurrent Neural Network (RNN), and the rendering of VR content is moved from VR device to MEC server with rendering model migration capability. Taking into account the geographical and FoV request correlation, we propose centralized and distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize the long-term QoE of VR users under the VR interaction latency constraint. Simulation results show that our proposed MEC rendering schemes and DRL algorithms substantially improve the long-term QoE of VR users and reduce the VR interaction latency compared to rendering at VR device

QoE Driven VR 360 Video Massive MIMO Transmission

Massive multiple-input and multiple-output (MIMO) enables ultra-high throughput and low latency for tile-based adaptive virtual reality (VR) 360 video transmission in wireless network. In this paper, we consider a massive MIMO system where multiple users in a single-cell theater watch an identical VR 360 video. Based on tile prediction, base station (BS) deliveries the tiles in predicted field of view (FoV) to users. By introducing practical supplementary transmission for missing tiles and unacceptable VR sickness, we propose the first stable transmission scheme for VR video. we formulate an integer non-linear programming (INLP) problem to maximize users' average quality of experience (QoE) score. Moreover, we derive the achievable spectral efficiency (SE) expression of predictive tile groups and the approximately achievable SE expression of missing tile groups, respectively. Analytically, the overall throughput is related to the number of tile groups and the length of pilot sequences. By exploiting the relationship between the structure of viewport tiles and SE expression, we propose a multi-lattice multi-stream grouping method aimed at improving the overall throughput for VR video transmission. Moreover, we analyze the relationship between QoE objective and number of predictive tile. We transform the original INLP problem into an integer linear programming problem by setting the predictive tiles groups as some constants. With variable relaxation and recovery, we obtain the optimal average QoE. Extensive simulation results validate that the proposed algorithm effectively improves QoE.Comment: Acceptede by IEEE transactions on wireless communication

Optimal Transmission of Multi-Quality Tiled 360 VR Video in MIMO-OFDMA Systems

In this paper, we study the optimal transmission of a multi-quality tiled 360 virtual reality (VR) video from a multi-antenna server (e.g., access point or base station) to multiple single-antenna users in a multiple-input multiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA) system. We minimize the total transmission power with respect to the subcarrier allocation constraints, rate allocation constraints, and successful transmission constraints, by optimizing the beamforming vector and subcarrier, transmission power and rate allocation. The formulated resource allocation problem is a challenging mixed discrete-continuous optimization problem. We obtain an asymptotically optimal solution in the case of a large antenna array, and a suboptimal solution in the general case. As far as we know, this is the first work providing optimization-based design for 360 VR video transmission in MIMO-OFDMA systems. Finally, by numerical results, we show that the proposed solutions achieve significant improvement in performance compared to the existing solutions.Comment: 6 pages, 4 figures, to appear in IEEE ICC 202

Optimal Multi-View Video Transmission in Multiuser Wireless Networks by Exploiting Natural and View Synthesis-Enabled Multicast Opportunities

Multi-view videos (MVVs) provide immersive viewing experience, at the cost of traffic load increase for wireless networks. In this paper, we would like to optimize MVV transmission in a multiuser wireless network by exploiting both natural multicast opportunities and view synthesis-enabled multicast opportunities. Specifically, we first establish a mathematical model to specify view synthesis at the server and each user, and characterize its impact on multicast opportunities. This model is highly nontrivial and fundamentally enables the optimization of view synthesis-based multicast opportunities. For given video quality requirements of all users, we consider the optimization of view selection, transmission time and power allocation to minimize the average weighted sum energy consumption for view transmission and synthesis. In addition, under the energy consumption constraints at the server and each user respectively, we consider the optimization of view selection, transmission time and power allocation and video quality selection to maximize the total utility. These two optimization problems are challenging mixed discrete-continuous optimization problems. For the first problem, we propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. For each problem, to reduce computational complexity, we also propose a low-complexity algorithm to obtain a suboptimal solution, using Difference of Convex (DC) programming. Finally, numerical results show the advantage of the proposed solutions over existing ones, and demonstrate the importance of the optimization of view synthesis-enabled multicast opportunities in MVV transmission.Comment: to be appear in IEEE Transactions on Communications. arXiv admin note: text overlap with arXiv:1808.0511

Optimal User-Cell Association for 360 Video Streaming over Dense Wireless Networks

Delivering 360 degree video streaming for virtual and augmented reality presents many technical challenges especially in bandwidth starved wireless environments. Recently, a so-called two-tier approach has been proposed which delivers a basic-tier chunk and select enhancement-tier chunks to improve user experience while reducing network resources consumption. The video chunks are to be transmitted via unicast or multicast over an ultra-dense small cell infrastructure with enough bandwidth where small cells store video chunks in local caches. In this setup, user-cell association algorithms play a central role to efficiently deliver video since users may only download video chunks from the cell they are associated with. Motivated by this, we jointly formulate the problem of user-cell association and video chunk multicasting/unicasting as a mixed integer linear programming, prove its NP-hardness, and study the optimal solution via the Branch-and-Bound method. We then propose two polynomial-time, approximation algorithms and show via extensive simulations that they are near-optimal in practice and improve user experience by 30% compared to baseline user-cell association schemes.Comment: 12 page