The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Neural Adaptive Video Streaming with Pensieve
Open accessAbstract
Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). Despite the abundance of recently proposed schemes, state-of-the-art ABR algorithms suffer from a key limitation: they use fixed control rules based on simplified or inaccurate models of the deployment environment. As a result, existing schemes inevitably fail to achieve optimal performance across a broad set of network conditions and QoE objectives.
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Cites
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Allman, M., Paxson, V., & Blanton, E. (2009) · doi:10.17487/rfc5681 · Cited by 2,523
Resource Management with Deep Reinforcement Learning
Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016) · Proceedings of the 15th ACM Workshop on Hot Topics in Networks · doi:10.1145/3005745.3005750 · Cited by 1,353 · cited 2×
“Pensieve uses modern reinforcement learning (RL) techniques [27, 29, 42] to learn a control policy for bitrate adaptation purely through experience.”
MethodSelf-citationCS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction
Sun, Y., Yin, X., Jiang, J., Sekar, V., Lin, F., Wang, N., Liu, T., & Sinopoli, B. (2016) · Proceedings of the 2016 ACM SIGCOMM Conference · doi:10.1145/2934872.2934898 · Cited by 407
Cited by
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Edge Assisted Real-time Object Detection for Mobile Augmented Reality
Liu, L., Li, H., & Gruteser, M. (2019) · The 25th Annual International Conference on Mobile Computing and Networking · doi:10.1145/3300061.3300116 · Cited by 552
AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization
Chen, L., Lingys, J., Chen, K., & Liu, F. (2018) · Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication · doi:10.1145/3230543.3230551 · Cited by 282
Deep learning for edge computing applications: A State-of-the-Art survey
Wang, F., Zhang, M., Wang, X., Ma, X., & Liu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2982411 · Cited by 259
Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197
Metadata
- Authors
- Mao, H., Netravali, R., & Alizadeh, M.
- Venue
- Proceedings of the Conference of the ACM Special Interest Group on Data Communication
- Publisher
- ACM
- Published
- 2017-08-07
- Pages
- 197-210
- OpenAlex
- W2744628735
- Cited by
- 1,357
- References
- 5
- FWCI
- 34.38
- Type
- conference paper
- Language
- en
- Open access
- green
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- Mao, H., Netravali, R., & Alizadeh, M. (2017). Neural Adaptive Video Streaming with Pensieve. Proceedings of the Conference of the ACM Special Interest Group on Data Communication. https://doi.org/10.1145/3098822.3098843
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