# Filling the gaps in video transcoder deployment in the cloud

arXiv: https://arxiv.org/abs/2304.08634 · HTML: https://mlsanity.com/paper/2304.08634/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2023.

## Built on by (most cited first, 1 of 1)

- [Predicting total time to compress a video corpus using online inference systems](https://mlsanity.com/paper/2410.18260/) (2024)

## Abstract (arXiv)

Cloud-based deployment of content production and broadcast workflows has continued to disrupt the industry after the pandemic. The key tools required for unlocking cloud workflows, e.g., transcoding, metadata parsing, and streaming playback, are increasingly commoditized. However, as video traffic continues to increase there is a need to consider tools which offer opportunities for further bitrate/quality gains as well as those which facilitate cloud deployment. In this paper we consider preprocessing, rate/distortion optimisation and cloud cost prediction tools which are only just emerging from the research community. These tools are posed as part of the per-clip optimisation approach to transcoding which has been adopted by large streaming media processing entities but has yet to be made more widely available for the industry.