LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models
Zhiyuan He Aashish Gottipati Lili Qiu Francis Y. Yan Xufang Luo Kenuo Xu Yuqing Yang
We present LLM-ABR, the first system that utilizes the generative
capabilities of large language models (LLMs) to autonomously design adaptive
bitrate (ABR) algorithms tailored for diverse network characteristics.
Operating within a reinforcement learning framework, LLM-ABR empowers LLMs to
design key components such as states and neural network architectures. We
evaluate LLM-ABR across diverse network settings, including broadband,
satellite, 4G, and 5G. LLM-ABR consistently outperforms default ABR algorithms.