title: LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models
summary: 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_authors._id: 660cbfd83e460eb2e0689c16
paper_authors.name: Zhiyuan He
paper_authors._id: 660cbfd83e460eb2e0689c17
paper_authors.name: Aashish Gottipati
paper_authors._id: 660cbfd83e460eb2e0689c18
paper_authors.name: Lili Qiu
paper_authors._id: 660cbfd83e460eb2e0689c19
paper_authors.name: Francis Y. Yan
paper_authors._id: 660cbfd83e460eb2e0689c1a
paper_authors.name: Xufang Luo
paper_authors._id: 660cbfd83e460eb2e0689c1b
paper_authors.name: Kenuo Xu
paper_authors._id: 660cbfd83e460eb2e0689c1c
paper_authors.name: Yuqing Yang
paper_title: LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models
paper_summary: 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_ai_keywords: large language models (LLMs) adaptive bitrate (ABR) reinforcement learning neural network architectures