Abstract
Video is by far the “biggest” Big Data, stretching network and storage capacity to their limits. To handle the situation, video compression has been an active field of study for many years, producing output of huge commercial interest, e.g., MPEG-2 and DVD. However, video coding is a computationally expensive process and for this reason, parallelization was proposed at various granularity levels. Of particular interest, are block level methods implemented in HEVC (High Efficiency Video Coding) which was designed to be the successor of H.264/AVC for the 4K era. Parallelization in HEVC is supported by the following three modes: slices, tiles and wavefront. While considerable research was conducted on the parallelization options of HEVC, it was focused on the case of homogeneous processors. In this paper we consider video coding parallelization when the processing elements are heterogeneous. In particular, we focus on wavefront and tile parallelism and measure the performance of scheduling schemes for the induced subtasks. Through simulation experiments with dataset values obtained from common benchmark sequences, we conclude on the relevant merits of the evaluated scheduling algorithms.
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