Enabling Quality-Driven Scalable Video Transmission over Multi-User NOMA System

Recently, non-orthogonal multiple access (NOMA) has been proposed to achieve higher spectral efficiency over conventional orthogonal multiple access. Although it has the potential to meet increasing demands of video services, it is still challenging to provide high performance video streaming. In this research, we investigate, for the first time, a multi-user NOMA system design for video transmission. Various NOMA systems have been proposed for data transmission in terms of throughput or reliability. However, the perceived quality, or the quality-of-experience of users, is more critical for video transmission. Based on this observation, we design a quality-driven scalable video transmission framework with cross-layer support for multi-user NOMA. To enable low complexity multi-user NOMA operations, a novel user grouping strategy is proposed. The key features in the proposed framework include the integration of the quality model for encoded video with the physical layer model for NOMA transmission, and the formulation of multi-user NOMA-based video transmission as a quality-driven power allocation problem. As the problem is non-concave, a global optimal algorithm based on the hidden monotonic property and a suboptimal algorithm with polynomial time complexity are developed. Simulation results show that the proposed multi-user NOMA system outperforms existing schemes in various video delivery scenarios.Comment: 9 pages, 6 figures. This paper has already been accepted by IEEE INFOCOM 201

DeepWiVe: deep-learning-aided wireless video transmission

We present DeepWiVe , the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves the video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize the overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect , which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 in terms of the multi-scale structural similarity index measure (MS-SSIM), and H.265+ LDPC by up to 0.0069 on average. We also illustrate the importance of optimizing bandwidth allocation in JSCC video transmission by showing that our optimal bandwidth allocation policy is superior to uniform allocation as well as a heuristic policy benchmark

A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams

Cross-layer Optimization for Video Delivery over Wireless Networks

As video streaming is becoming the most popular application of Internet mo- bile, the design and the optimization of video communications over wireless networks is attracting increasingly attention from both academia and indus- try. The main challenges are to enhance the quality of service support, and to dynamically adapt the transmitted video streams to the network condition. The cross-layer methods, i.e., the exchange of information among different layers of the system, is one of the key concepts to be exploited to achieve this goals. In this thesis we propose novel cross-layer optimization frameworks for scalable video coding (SVC) delivery and for HTTP adaptive streaming (HAS) application over the downlink and the uplink of Long Term Evolution (LTE) wireless networks. They jointly address optimized content-aware rate adaptation and radio resource allocation (RRA) with the aim of maximiz- ing the sum of the achievable rates while minimizing the quality difference among multiple videos. For multi-user SVC delivery over downlink wireless systems, where IP/TV is the most representative application, we decompose the optimization problem and we propose the novel iterative local approxi- mation algorithm to derive the optimal solution, by also presenting optimal algorithms to solve the resulting two sub-problems. For multiple SVC de- livery over uplink wireless systems, where healt-care services are the most attractive and challenging application, we propose joint video adaptation and aggregation directly performed at the application layer of the transmit- ting equipment, which exploits the guaranteed bit-rate (GBR) provided by the low-complexity sub-optimal RRA solutions proposed. Finally, we pro- pose a quality-fair adaptive streaming solution to deliver fair video quality to HAS clients in a LTE cell by adaptively selecting the prescribed (GBR) of each user according to the video content in addition to the channel condi- tion. Extensive numerical evaluations show the significant enhancements of the proposed strategies with respect to other state-of-the-art frameworks

Source video rate allocation and scheduling policy design for wireless networks

In this work we address the problem of designing an efficient algorithm that allows high quality real time video streaming, from the source coding and scheduling perspectives. We take into account some key quantities like frame correlation, channel constraints and QoS metrics such as distortion and PSNR. We first formalize the problem in a mathematical fashion and then propose a Markov-chain based solution that applies to quantized values of the metrics involved in the framewor