Rate Control Initialization Algorithm for Scalable Video Coding

Proceeding of: 18th IEEE International Conference on Image Processing (ICIP), 2011.In this paper we propose a novel rate control initialization algorithm for real-time H.264/scalable video coding. In particular, a two-step approach is proposed. First, the initial quantization parameter (QP) for each layer is determined by means of a parametric rate-quantization (R-Q) modeling that depends on the layer identifier (base or enhancement) and on the type of scalability (spatial or quality). Second, an intra-frame QP refinement method that allows for adapting the initial QP value when needed is carried out over the three first coded frames in order to take into consideration both the buffer control and the spatio-temporal complexity of the scene. The experimental results show that the proposed R-Q modeling for initial QP estimation, in combination with the intra-frame QP refinement method, provide a good performance in terms of visual quality and buffer control, achieving remarkably similar results to those achieved by using ideal initial QP values.The Spanish National grant TSI-020110-2009-103 (AFICUS) and the Regional grant CCG10-UC3M/TIC-5570 (AMASSACA).Publicad

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

Tracking-Optimized Quantization for H.264 Compression in Transportation Video Surveillance Applications

We propose a tracking-aware system that removes video components of low tracking interest and optimizes the quantization during compression of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. The process of optimizing quantization tables suitable for automated tracking can be executed online or offline. The online implementation initializes the encoding procedure for a specific scene, but introduces delay. On the other hand, the offline procedure produces globally optimum quantization tables where the optimization occurs for a collection of video sequences. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that while maintaining comparable tracking accuracy our system allows for over 50% bitrate savings on top of existing savings from previous work

NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming

In recent years, there has been an increasing demand for ubiquitous streaming like applications in data networks. In this paper, we concentrate on NUM-based rate allocation for streaming applications with the so-called S-curve utility functions. Due to non-concavity of such utility functions, the underlying NUM problem would be non-convex for which dual methods might become quite useless. To tackle the non-convex problem, using elementary techniques we make the utility of the network concave, however this results in reverse-convex constraints which make the problem non-convex. To deal with such a transformed NUM, we leverage Sequential Convex Programming (SCP) approach to approximate the non-convex problem by a series of convex ones. Based on this approach, we propose a distributed rate allocation algorithm and demonstrate that under mild conditions, it converges to a locally optimal solution of the original NUM. Numerical results validate the effectiveness, in terms of tractable convergence of the proposed rate allocation algorithm.Comment: 6 pages, conference submissio