LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models

arXiv:2404.01617v1 Announce Type: cross
Abstract: 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.

abstract algorithms arxiv capabilities components cs.lg cs.mm cs.ni design designing diverse framework generative key language language models large language large language models llm llms network reinforcement reinforcement learning type via