# Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

arXiv: https://arxiv.org/abs/1711.01008 · HTML: https://mlsanity.com/paper/1711.01008/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://mlsanity.com/paper/2106.03727/) (2021)
- [Predicting total time to compress a video corpus using online inference systems](https://mlsanity.com/paper/2410.18260/) (2024)

## Abstract (arXiv)

Video streams usually have to be transcoded to match the characteristics of viewers' devices. Streaming providers have to store numerous transcoded versions of a given video to serve various display devices. Given the fact that viewers' access pattern to video streams follows a long tail distribution, for the video streams with low access rate, we propose to transcode them in an on-demand manner using cloud computing services. The challenge in utilizing cloud services for on-demand video transcoding is to maintain a robust QoS for viewers and cost-efficiency for streaming service providers. To address this challenge, we present the Cloud-based Video Streaming Services (CVS2) architecture. It includes a QoS-aware scheduling that maps transcoding tasks to the VMs by considering the affinity of the transcoding tasks with the allocated heterogeneous VMs. To maintain robustness in the presence of varying streaming requests, the architecture includes a cost-efficient VM Provisioner. This component provides a self- configurable cluster of heterogeneous VMs. The cluster is reconfigured dynamically to maintain the maximum affinity with the arriving workload. Results obtained under diverse workload conditions demonstrate that CVS2 architecture can maintain a robust QoS for viewers while reducing the incurred cost of the streaming service provider up to 85%