Adaptive Configuration of Cloud Video Transcoding

Cloud computing is emerging as a new paradigm which enables big data computing, including high quality media processing. However, considering the media dynamics on resource consumption and the QoS criteria, dynamically providing the cloud computing resource to meet the QoS requirements of media processing is not easy. The current cloud computing infrastructure usually employs auto-scaling to dynamically adjust the computing resource allocation, which is typically performed at relatively long time scale and cannot adapt to the dynamic changes of video arrivals or content changes at relatively short time scale. In this paper, we propose to adaptively configure the video transcoding mode to deal with the short-term transcoding QoS and computing resource mismatch problem. We formulate the problem as the one to minimize the output bit-rate with the queue stability constraint, for which we use the Lyapunov optimization framework to solve it. Simulation results show that, compared with the static configuration strategy, the proposed adaptive method achieves smooth transcoding QoS degradation when system load becomes heavier and much better transcoding delay performance.