LLM-ABR：利用大型语言模型设计自适应码率算法
LLM-ABR: Designing Adaptive Bitrate Algorithms via Large Language Models
April 2, 2024
作者: Zhiyuan He, Aashish Gottipati, Lili Qiu, Francis Y. Yan, Xufang Luo, Kenuo Xu, Yuqing Yang
cs.AI
摘要
我们提出了LLM-ABR，这是首个利用大型语言模型（LLMs）的生成能力，自主设计适应性比特率（ABR）算法的系统，该算法针对多样化的网络特性进行了定制。在强化学习框架内运行，LLM-ABR使LLMs能够设计关键组件，如状态和神经网络架构。我们在多种网络环境下评估了LLM-ABR，包括宽带、卫星、4G和5G。LLM-ABR始终优于默认的ABR算法。
English
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.