Energy efficient video decoding on multi-core devices

Abstract

Emergence of high quality media applications results in larger data sizes as well as higher bitrates of digital multimedia contents, and their significant share on the overall Internet traffic. These lead to an increase in the energy consumption rates and performance requirements for real-time video decoding. In this study, we propose parallel video decoding solutions to provide real-time decoding performance with reduced energy consumption on multi-core devices. Various approaches of parallelism at data and task levels can be incorporated in video decoders, bringing efficiency in energy consumption rates and/or performance. We offer and develop two approaches for the H.264 standard. The former is based on a coarse-grained frame level, and the latter is a fine-grained macroblock level parallelism. The implementations were conducted on a shared memory multi-core platform as an all software solution for real-time scalable video decoding. We also discuss energy efficiency as well as performance results. As part of our ongoing work, further parallelization methods such as parallelism at slice level, and parallel decoding of consecutive groups of pictures on the H.264/SVC decoder are discussed