Quality-Aware Transcoding Task Allocation Under Limited Power in Live-Streaming Systems

Transcoding in video live-streaming systems requires a lot of computation and, hence, a lot of power. Putting a limit on the power drawn by each of the transcoding processors in a server reduces the overall power consumption, but it also hinders the efficient allocation of transcoding tasks. We address this with a dynamic programming algorithm, together with a heuristic, which maximizes total processing capacity while limiting power consumption in a server with heterogeneous processors. A further greedy algorithm determines the bitrates, at which content is transcoded for each channel, and allocates transcoding tasks to processors, while taking video quality, popularity, and workload balance into account. The initial assumption is that all contents are transcoded to all bitrates for every channel. Then, the algorithm gradually reduces the number of versions to be produced by transcoding, while minimizing the consequent reduction in popularity-weighted video quality, as well as balancing the workload across processors. Experimental results show that our scheme improves aggregate popularity-weighted video quality under a power constraint by 3.82%–39.12%, compared to benchmark methods.