# Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning

arXiv: https://arxiv.org/abs/1908.02270 · HTML: https://mlsanity.com/paper/1908.02270/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?](https://mlsanity.com/paper/2503.12496/) (2025)
- [Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks](https://mlsanity.com/paper/2502.05695/) (2025)
- [Beyond Interpretability: Exploring the Comprehensibility of Adaptive Video Streaming through Large Language Models](https://mlsanity.com/paper/2508.16448/) (2025)
- [ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming](https://mlsanity.com/paper/2104.12507/) (2021)
- [A review on Machine Learning based User-Centric Multimedia Streaming Techniques](https://mlsanity.com/paper/2411.15801/) (2024)
- [Multi-Objective Pareto-Front Optimization for Efficient Adaptive VVC Streaming](https://mlsanity.com/paper/2601.10607/) (2026)
- [Karma: Adaptive Video Streaming via Causal Sequence Modeling](https://mlsanity.com/paper/2308.10230/) (2023)
- [LL-GABR: Energy Efficient Live Video Streaming Using Reinforcement Learning](https://mlsanity.com/paper/2402.09392/) (2024)
- [SABR: A Stable Adaptive Bitrate Framework Using Behavior Cloning Pretraining and Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2509.10486/) (2025)
- [HiVid: LLM-Guided Video Saliency For Content-Aware VOD And Live Streaming](https://mlsanity.com/paper/2602.14214/) (2026)
- [NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift](https://mlsanity.com/paper/2607.18845/) (2026)

## Abstract (arXiv)

Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. However, it typically suffers from several issues, i.e., low sample efficiency and lack of awareness of the video quality information. In this paper, we propose Comyco, a video quality-aware ABR approach that enormously improves the learning-based methods by tackling the above issues. Comyco trains the policy via imitating expert trajectories given by the instant solver, which can not only avoid redundant exploration but also make better use of the collected samples. Meanwhile, Comyco attempts to pick the chunk with higher perceptual video qualities rather than video bitrates. To achieve this, we construct Comyco's neural network architecture, video datasets and QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required. Moreover, results illustrate that Comyco outperforms previously proposed methods, with the improvements on average QoE of 7.5% - 16.79%. Especially, Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average video quality under the same rebuffering time.