LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models
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.
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Zhiyuan He
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Aashish Gottipati
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Lili Qiu
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Francis Y. Yan
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Xufang Luo
660cbfd83e460eb2e0689c1b
Kenuo Xu
660cbfd83e460eb2e0689c1c
Yuqing Yang
LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models
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.
LLM-ABR autonomously designs adaptive bitrate algorithms using large language models within a reinforcement learning framework, outperforming default algorithms across various network settings.
large language models LLM adaptive bitrate ABR reinforcement learning neural network architectures