Abstract
Efficient video transcoding has become a critical technology in content delivery network (CDN) service for social networked video application. In this paper, we propose a data-driven parallel video transcoding solution for CDN. The key to the solution is to compute the ranking of popular videos through web log analysis and then transcode popular videos by mapreduce. Experimental results show that our solution can significantly improve the efficiency of video transcoding.