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.