Wangyu Choi
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.
- Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural Adaptive Video Streaming with Pensieve. In Proceedings of the Conference of the ACM Speciallnterest Group on Data Communication (SIGCOMM '17). Association for Computing Machinery, New York, NY, USA, 197--210. Google Scholar
Digital Library
- Zahaib Akhtar, Yun Seong Nam, Ramesh Govindan, Sanjay Rao, Jessica Chen, Ethan Katz-Bassett, Bruno Ribeiro, Jibin Zhan, and Hui Zhang. 2018. Oboe: Auto-Tuning Video ABR Algorithms to Network Conditions. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (SIGCOMM '18). Association for Computing Machinery, New York, NY, USA, 44--58. Google ScholarDigital Library
- Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein. 2020. Learning in situ: a randomized experiment in video streaming. In Proceedings of the 17th Usenix Conference on Networked Systems Design and Implementation (NSDI'20). USENIX Association, USA, 495--512.Google Scholar
- Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML'17). JMLR.org, 1126--1135.Google Scholar
- John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. arXiv:1707.06347. Retrieved from https://arxiv.org/abs/1707.06347Google Scholar
- Te-Yuan Huang, Ramesh Johari, Nick McKeown, Matthew Trunnell, and Mark Watson. 2014. A Buffer-Based Approach to Rate Adaptation: Evidence from a Large Video Streaming Service. In Proceedings of the 2014 ACM Conference on Special Interest Group on Data Communication (SIGCOMM '14). Association for Computing Machinery, New York, NY, USA, 187--198. Google ScholarDigital Library
- Xiaoqi Yin, Abhishek Jindal, Vyas Sekar, and Bruno Sinopoli. 2015. A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP. In Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication (SIGCOMM '15). Association for Computing Machinery, New York, NY, USA, 325--338. Google ScholarDigital Library
Index Terms
- MetaABR: environment-adaptive video streaming system with meta-reinforcement learning
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