Cost optimal video transcoding in media cloud: Insights from user viewing pattern

Video transcoding has been touted as an enabling technology to support growing media consumption over heterogenous devices. However, on-line transcoding could incur tremendous, if not prohibitive, cost in deploying or renting resources. In this research, we leverage an insight into the viewing pattern of video consumers to reduce the operating cost of video transcoding services. Specifically, it has been reported that viewers tend to terminate their session before the whole video is watched. As such, it is not cost-efficient for service providers to store or transcode all segments of the videos. Built upon this insight, we propose a partial transcoding scheme for content management in a media cloud to reduce the operating cost. Particularly, each content is split into multiple segments and stored in different files of varying playback rates. Some of the segments are stored in cache, resulting in storage cost; while some are transcoded in real-time in case of cache miss, resulting in computing cost. We aim to minimize the long-term operational cost by determining the number of segments for each playback rate to be cached or transcoded in real-time. We formulate this partial transcoding scheme as a constrained integer optimization problem. Leveraging Lagrangian relaxation and a subgradient method, we obtain the approximate solution to the integer program. Numerical results indicate that our proposed partial transcoding scheme can save more than 30% of operational cost, compared with a brute-force scheme of caching all the segments.