A MOBILE CROWDSOURCING SYSTEM FOR FOOTBALL MATCH LIVE VIDEO STREAMING

Mobile crowdsourcing is a fast-growing emerging approach whereby large groups of mobile users are engaged in a collaborative work on performing a particular task or using its results. This paper presents a concept for the development of a mobile crowdsourcing system with extended capabilities for real-time broadcasting and receiving amateur football match video. It is designed to resolve the problem of possible delays and the overload of a system and to accelerate the process of big video data transmission. The proposed system is based on a service-oriented, three-layer cloud architecture and a specialized mobile video streaming application. The architecture includes a main server, infrastructure of scalable multi-parallel video processing engine and an auxiliary server for synchronizing real-time information, which significantly facilitates the handling of user requests with minimal cost and at a high speed. The concept is realized in the Footlikers platform as a basic client-server, WOWZA streaming engine, deployed on an Amazon EC2 cloud machine and a simple sync-server. The results of the program realization of the developed system prototype are presented, regarding football game video steaming intended for amateur football competitions based and organized in France, Belgium and Luxembourg.

Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation

Through reorganizing the execution order and optimizing the data structure, we proposed an efficient parallel framework for H.264/AVC encoder based on massively parallel architecture. We implemented the proposed framework by CUDA on NVIDIA’s GPU. Not only the compute intensive components of the H.264 encoder are parallelized but also the control intensive components are realized effectively, such as CAVLC and deblocking filter. In addition, we proposed serial optimization methods, including the multiresolution multiwindow for motion estimation, multilevel parallel strategy to enhance the parallelism of intracoding as much as possible, component-based parallel CAVLC, and direction-priority deblocking filter. More than 96% of workload of H.264 encoder is offloaded to GPU. Experimental results show that the parallel implementation outperforms the serial program by 20 times of speedup ratio and satisfies the requirement of the real-time HD encoding of 30 fps. The loss of PSNR is from 0.14 dB to 0.77 dB, when keeping the same bitrate. Through the analysis to the kernels, we found that speedup ratios of the compute intensive algorithms are proportional with the computation power of the GPU. However, the performance of the control intensive parts (CAVLC) is much related to the memory bandwidth, which gives an insight for new architecture design.