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The growth in video Internet traffic and advancements in video attributessuch as framerate, resolution, and bit-depth boost the demand to devise alarge-scale, highly efficient video encoding environment. This is even moreessential for Dynamic Adaptive Streaming over HTTP (DASH)-based contentprovisioning as it requires encoding numerous representations of the same videocontent. High Efficiency Video Coding (HEVC) is one standard video codec thatsignificantly improves encoding efficiency over its predecessor Advanced VideoCoding (AVC). This improvement is achieved at the expense of significantlyincreased time complexity, which is a challenge for content and serviceproviders. As various representations are the same video content encoded atdifferent bitrates or resolutions, the encoding analysis information from thealready encoded representations can be shared to accelerate the encoding ofother representations. Several state-of-the-art schemes first encode a singlerepresentation, called a reference representation. During this encoding, theencoder creates analysis metadata with information such as the slicetypedecisions, CU, PU, TU partitioning, and the HEVC bitstream itself. Theremaining representations, called dependent representations, analyze the abovemetadata and then reuse it to skip searching some partitioning, thus, reducingthe computational complexity. With the emergence of cloud-based encodingservices, video encoding is accelerated by utilizing an increased number ofresources, i.e., with multi-core CPUs, multiple representations can be encodedin parallel. This paper presents an overview of a wide range of multi-encodingschemes with and without the support of machine learning approaches integratedinto the HEVC Test Model (HM) and x265, respectively.

Fast multi-encoding to reduce the cost of video streaming