# QARC: Video Quality Aware Rate Control for Real-Time Video Streaming via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1805.02482 · HTML: https://mlsanity.com/paper/1805.02482/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2018.

## Built on by (most cited first, 7 of 7)

- [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://mlsanity.com/paper/2106.03727/) (2021)
- [Edge-Based Video Analytics: A Survey](https://mlsanity.com/paper/2303.14329/) (2023)
- [ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming](https://mlsanity.com/paper/2104.12507/) (2021)
- [NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video Streaming](https://mlsanity.com/paper/2107.07127/) (2021)
- [RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols](https://mlsanity.com/paper/2302.04519/) (2023)
- [Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events](https://mlsanity.com/paper/2111.09412/) (2021)
- [Enabling Real-time Neural Recovery for Cloud Gaming on Mobile Devices](https://mlsanity.com/paper/2307.07847/) (2023)

## Abstract (arXiv)

Due to the fluctuation of throughput under various network conditions, how to choose a proper bitrate adaptively for real-time video streaming has become an upcoming and interesting issue. Recent work focuses on providing high video bitrates instead of video qualities. Nevertheless, we notice that there exists a trade-off between sending bitrate and video quality, which motivates us to focus on how to get a balance between them. In this paper, we propose QARC (video Quality Awareness Rate Control), a rate control algorithm that aims to have a higher perceptual video quality with possibly lower sending rate and transmission latency. Starting from scratch, QARC uses deep reinforcement learning(DRL) algorithm to train a neural network to select future bitrates based on previously observed network status and past video frames, and we design a neural network to predict future perceptual video quality as a vector for taking the place of the raw picture in the DRL's inputs. We evaluate QARC over a trace-driven emulation. As excepted, QARC betters existing approaches.