Video decoding applications must often cope with highly time-varying workload demands, while meeting stringent display deadlines. Voltage/frequency scalable processors are highly attractive for video decoding on resource-constrained systems, since significant energy savings can be achieved by dynamically adapting the processor speed based on the changing workload demand. Previous works on video-related voltage scaling algorithms are often limited by the lack of a good complexity model for video and often do not explicitly consider the video quality impact of various steps involved in the decoding process. Our contribution in this paper is threefold. First, we propose a novel complexity model through offline training that explicitly considers the video source characteristics, the encoding algorithm, and platform specifics to predict execution times. Second, based on the complexity model, we propose low complexity online voltage scaling algorithms to process decoding jobs such that they meet their display deadlines with high probability. We show that on average, our queuing-based voltage scaling algorithm provides approximately 10%-15% energy savings over existing voltage scaling algorithms. Finally, we propose a joint voltage scaling and quality-aware priority scheduling algorithm that decodes jobs in order of their distortion impact, such that by setting the processor to various power levels and decoding only the jobs that contribute most to the overall quality, efficient quality, and energy tradeoffs can be achieved. We demonstrate the scalability of our algorithm in various practical decoding scenarios, where reducing the power to 25% of the original power can lead to quality degradations of less than 1.0 dB PSNR.
['Brian Foo', 'M. van der Schaar']
A Queuing Theoretic Approach to Processor Power Adaptation for Video Decoding Systems