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

LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models: 6 upvotes on Hugging Face Daily Papers, #10 of 11 papers on 2024-04-03. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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