@inproceedings{
zhang2024aralive,
title={AraLive: Automatic Reward Adaption for Learning-based Live Video Streaming},
author={Huanhuan Zhang and Zhuo Liu and Haotian Li and Anfu Zhou and Chuanming Wang and Huadong Ma},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=4dwZARybRf}
}
Optimizing user Quality of Experience (QoE) for live video streaming remains a long-standing challenge. The Bitrate Control Algorithm (BCA) plays a crucial role in shaping user QoE. Recent advancements have seen RL-based algorithms overtake traditional rule-based methods, promising enhanced QoE optimization. Nevertheless, our comprehensive study reveals a pressing issue: current RL-based BCAs are limited to the fixed and formulaic reward functions, rendering them ill-equipped to adapt to dynamic network environments and varied viewer preferences. In this work, we present AraLive, an automatically adaptive reward learning method designed for seamless integration with any existing learning-based approach in live streaming contexts. To accomplish this goal, we construct a dedicated user QoE assessment dataset for live streaming and customize-design an adversarial model that skillfully aligns human feedback with actual network scenarios. We have deployed AraLive in not only the live streaming but also the classic VoD systems, in comparison to a series of state-of-the-art BCAs. The experimental results demonstrate that AraLive not only elevates overall QoE but also exhibits remarkable adaptability to varied user preferences.
AraLive: Automatic Reward Adaption for Learning-based Live Video Streaming
Huanhuan Zhang Zhuo Liu Haotian Li Anfu Zhou Chuanming Wang Huadong Ma