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 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

Viewport Adaptation-Based Immersive Video Streaming: Perceptual Modeling and Applications

Immersive video offers the freedom to navigate inside virtualized environment. Instead of streaming the bulky immersive videos entirely, a viewport (also referred to as field of view, FoV) adaptive streaming is preferred. We often stream the high-quality content within current viewport, while reducing the quality of representation elsewhere to save the network bandwidth consumption. Consider that we could refine the quality when focusing on a new FoV, in this paper, we model the perceptual impact of the quality variations (through adapting the quantization stepsize and spatial resolution) with respect to the refinement duration, and yield a product of two closed-form exponential functions that well explain the joint quantization and resolution induced quality impact. Analytical model is cross-validated using another set of data, where both Pearson and Spearman's rank correlation coefficients are close to 0.98. Our work is devised to optimize the adaptive FoV streaming of the immersive video under limited network resource. Numerical results show that our proposed model significantly improves the quality of experience of users, with about 9.36\% BD-Rate (Bjontegaard Delta Rate) improvement on average as compared to other representative methods, particularly under the limited bandwidth.Comment: 12 page

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

Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning

This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance their immersive visual experiences, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited. Owing to user movement and time-varying wireless channels, the wireless VR experience enhancement problem is formulated as a sequence-dependent and mixed-integer problem with a goal of maximizing users' feeling of presence (FoP) in the virtual world, subject to power consumption constraints on access points (APs) and users' head-mounted displays (HMDs). The problem, however, is hard to be directly solved due to the lack of users' accurate tracking information and the sequence-dependent and mixed-integer characteristics. To overcome this challenge, we develop a parallel echo state network (ESN) learning method to predict users' tracking information by training fresh and historical tracking samples separately collected by APs. With the learnt results, we propose a deep reinforcement learning (DRL) based optimization algorithm to solve the formulated problem. In this algorithm, we implement deep neural networks (DNNs) as a scalable solution to produce integer decision variables and solving a continuous power control problem to criticize the integer decision variables. Finally, the performance of the proposed algorithm is compared with various benchmark algorithms, and the impact of different design parameters is also discussed. Simulation results demonstrate that the proposed algorithm is more 4.14% energy-efficient than the benchmark algorithms

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

Millimetre wave communications in 5G networks under latency constraints: machine intelligence, application scenarios and perspectives.

222 p.Nowadays there is little doubt that wireless communications have been a pivotal player in the irruption and maturity of digital technologies in almost all sectors of activity. Over the years, the society has witnessed how wireless networking has become an essential element of its evolution and prosperity. However, the ever-growing requirements of applications and services in terms of rate, reliability and latency have steered the interest of regulatory bodies towards emergent radio access interfaces capable of efficiently coping with such requisites. In this context, millimeter-wave (mmWave) communications have been widely acknowledged as a technology enabler for ultra-reliable, low-latency applications in forthcoming standards, such as 5G. Unfortunately, the unprecedented data rates delivered by mmWave communications come along with new paradigms in regards to radio resource allocation, user scheduling, and other issues all across the protocol stack, mainly due to the directivity of antennas andsensitiveness to blockage of communications held in this spectrum band. Consequently, the provision of machine intelligence to systems and processes relying on mmWave radio interfaces is a must for efficiently handling the aforementioned challenges.This Thesis contributes to the above research niche by exploring the use of elements and tools from Computational Intelligence, Matching Theory and Stochastic Optimization for the management of radio and network resources in mmWave communications. To this end, two different application scenarios are targeted: 1) Vehicular communications, where the high degree of mobility and the recurrent inter-vehicular blockage give rise to complex channel conditions for channel allocation and beam alignment; and 2) mobile virtual reality (VR), where the motion-to-photon latency limit raises the hurdle for scheduling the delivery of multimedia content over mmWave. A diversity of intelligent methods for clustering, predictive modeling, matching and optimization for dynamical systems are studied, adapted and applied to the aforementioned scenarios, giving evidences of the profitable advantages and performance gains yielded by these methods. The Thesis complements its technical contribution with a thorough overview of the recent literature of mmWave communications, leading to the main conclusion stemming from the findings of the Thesis: machine intelligence, provided by any technological means, is a driver to realize the enormous potential of mmWave for applications with unprecedented latency constraints