MetaABR: Environment-Adaptive Video Streaming System with Meta-Reinforcement Learning
- Title
- MetaABR: Environment-Adaptive Video Streaming System with Meta-Reinforcement Learning
- Author
- 윤종원
- Keywords
- Adaptive Bitrate Algorithm; Meta-reinforcement Learning
- Issue Date
- 2022-12
- Publisher
- ASSOC COMPUTING MACHINERY
- Citation
- Proceedings of the International CoNEXT Student Workshop 2022, Part CoNEXT 2022, Page. 37.0-39.0
- Abstract
- This work focuses on a video bitrate algorithm that quickly adapts to new and various environments with just a few update steps. This aspect is especially important for large-scale video streaming services used by a wide variety of users in different environments. Our proposed model is based on a neural network and employs meta-reinforcement learning to train it. After training, it can be easily customized for a variety of new environments with a few update steps, providing a user-specific streaming service.
- URI
- https://dl.acm.org/doi/abs/10.1145/3565477.3569155?https://repository.hanyang.ac.kr/handle/20.500.11754/187922
- DOI
- 10.1145/3565477.3569155
- Appears in Collections:
- ETC[S] > ETC
- Files in This Item:
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