MetaLive: Meta-Reinforcement Learning Based Collective Bitrate Adaptation for Multi-Party Live Streaming

Author:

Yang Yi,Li Xiang,Xu Yeting,Li Wenzhong,Hu Jiangyi,Xu Taishan,Ren Xiancheng,Lu Sanglu

Publisher

Springer Nature Switzerland

Reference19 articles.

1. Raw data - measuring broadband America (2016). https://www.fcc.gov/reports-research/reports/ (2021)

2. Al-Shedivat, M., Bansal, T., Burda, Y., Sutskever, I., Mordatch, I., Abbeel, P.: Continuous adaptation via meta-learning in nonstationary and competitive environments. In: 6th International Conference on Learning Representations, (ICLR 2018) (2018)

3. Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. CoRR abs/1601.00670 (2016)

4. Bokani, A., Hassan, M., Kanhere, S.S., Yao, J., Zhong, G.: Comprehensive mobile bandwidth traces from vehicular networks. In: Proceedings of the 7th International Conference on Multimedia Systems, pp. 1–6 (2016)

5. Gadaleta, M., Chiariotti, F., Rossi, M., Zanella, A.: D-dash: a deep q-learning framework for dash video streaming. IEEE Trans. Cogn. Commun. Netw. 3(4), 703–718 (2017)

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