Joint Source-Channel Coding for Real-Time Video Transmission to Multi-homed Mobile Terminals

This study focuses on the mobile video delivery from a video server to a multi-homed client with a network of heterogeneous wireless. Joint Source-Channel Coding is effectively used to transmit video over bandwidth-limited, noisy wireless networks. But most existing JSCC methods only consider single path video transmission of the server and the client network. The problem will become more complicated when consider multi-path video transmission, because involving low-bandwidth, high-drop-rate or high-latency wireless network will only reduce the video quality. To solve this critical problem, we propose a novel Path Adaption JSCC (PA-JSCC) method that contain below characters: (1) path adaption, and (2) dynamic rate allocation. We use Exata to evaluate the performance of PA-JSCC and Experiment show that PA-JSCC has a good results in terms of PSNR (Peak Signal-to-Noise Ratio).Comment: 5 pages. arXiv admin note: text overlap with arXiv:1406.7054 by other author

A QoS Guarantee Strategy for Multimedia Conferencing based on Bayesian Networks

Service Oriented Architecture (SOA) is commonly employed in the design and implementation of web service systems. The key technology to enable media communications in the context of SOA is the Service Oriented Communication. To exploit the advantage of SOA, we design and implement a web-based multimedia conferencing system that provides users with a hybrid orchestration of web and communication services. As the current SOA lacks effective QoS guarantee solutions for multimedia services, the user satisfaction is greatly challenged with QoS violations, e.g., low video PSNR (Peak Signal-to-Noise Ratio) and long playback delay. Motivated by addressing the critical problem, we firstly employ the Business Process Execution Language (BPEL) service engine for the hybrid services orchestration and execution. Secondly, we propose a novel context-aware approach to quantify and leverage the causal relationships between QoS metrics and available contexts based on Bayesian networks (CABIN). This approach includes three phases: (1) information discretization, (2) causal relationship profiling, and (3) optimal context tuning. We implement CABIN in a real-life multimedia conferencing system and compare its performance with existing delay and throughput oriented schemes. Experimental results show that CABIN outperforms the competing approaches in improving the video quality in terms of PSNR. It also provides a one-stop shop controls both the web and communication services.Comment: 9 page