ABSTRACT
In this paper, we introduce a continuous bitrate control and latency control model for the Live Video Streaming Challenge. Our model is based on Deep Deterministic Policy Gradient, popular on continuous control tasks. Simultaneously, it can take a fine-grained control through continuous control and does not need to discrete the continuous "latency limit", which is a buffer threshold to minimize end-to-end delay by frame skipping. In all considered live video scenarios, our model can provide a better quality of experience with improvements in average QoE of 3.6% than DQN which discrete the "latency limit". Additionally, challenge results show the effectiveness and applicability of the proposed model, which achieved top performance in 3 different networks that include high, low and oscillating throughput, and ranked the second place in the network with medium throughput.
- 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. ACM, 44--58.Google ScholarDigital Library
- Abdelhak Bentaleb, Bayan Taani, Ali C Begen, Christian Timmerer, and Roger Zimmermann. 2018. A survey on bitrate adaptation schemes for streaming media over http. IEEE Communications Surveys & Tutorials, Vol. 21, 1 (2018), 562--585.Google ScholarCross Ref
- VNI Cisco. 2018. Cisco Visual Networking Index: Forecast and Trends, 2017--2022. White Paper (2018).Google Scholar
- Matteo Gadaleta, Federico Chiariotti, Michele Rossi, and Andrea Zanella. 2017. D-DASH: A deep Q-learning framework for DASH video streaming. IEEE Transactions on Cognitive Communications and Networking, Vol. 3, 4 (2017), 703--718.Google ScholarCross Ref
- Tianchi Huang, Rui-Xiao Zhang, Zhou Chao, and Lifeng Sun. 2018. QARC: Video Quality Aware Rate Control for Real-Time Video Streaming based on Deep Reinforcement Learning. (2018).Google ScholarDigital Library
- 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. Acm Sigcomm Computer Communication Review, Vol. 44, 4 (2014), 187--198.Google ScholarDigital Library
- Junchen Jiang, Vyas Sekar, and Hui Zhang. 2014. Improving fairness, efficiency, and stability in http-based adaptive video streaming with festive. IEEE/ACM Transactions on Networking (ToN), Vol. 22, 1 (2014), 326--340.Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).Google Scholar
- Yuxi Li. 2017. Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274 (2017).Google Scholar
- Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015).Google Scholar
- Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural adaptive video streaming with pensieve. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication. ACM, 197--210.Google ScholarDigital Library
- Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International conference on machine learning. 1928--1937.Google ScholarDigital Library
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et almbox. 2015. Human-level control through deep reinforcement learning. Nature, Vol. 518, 7540 (2015), 529.Google Scholar
- Kevin Spiteri, Rahul Urgaonkar, and Ramesh K Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. IEEE, 1--9.Google ScholarDigital Library
- Yi Sun, Xiaoqi Yin, Junchen Jiang, Vyas Sekar, Fuyuan Lin, Nanshu Wang, Tao Liu, and Bruno Sinopoli. 2016. CS2P: Improving video bitrate selection and adaptation with data-driven throughput prediction. In Proceedings of the 2016 ACM SIGCOMM Conference. ACM, 272--285.Google ScholarDigital Library
- Pawel Wawrzynski. 2015. Control policy with autocorrelated noise in reinforcement learning for robotics. International Journal of Machine Learning and Computing, Vol. 5, 2 (2015), 91.Google ScholarCross Ref
- Xiaoqi Yin, Abhishek Jindal, Vyas Sekar, and Bruno Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. In ACM SIGCOMM Computer Communication Review, Vol. 45. ACM, 325--338.Google ScholarDigital Library
Index Terms
- Continuous Bitrate & Latency Control with Deep Reinforcement Learning for Live Video Streaming
Recommendations
The ACM Multimedia 2019 Live Video Streaming Grand Challenge
MM '19: Proceedings of the 27th ACM International Conference on MultimediaLive video streaming delivery over Dynamic Adaptive Video Streaming (DASH) is challenging as it requires low end-to-end latency, is more prone to stall, and the receiver has to decide online which representation at which bitrate to download and whether ...
Live Video Streaming Optimization Based on Deep Reinforcement Learning
ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and ComputingVideo players employ adaptive bitrate algorithms in video-on-demand (VoD) scenarios to improve user-perceived quality of experience (QoE), whereas performance will obviously decline in live video streaming scenarios. To this end, we propose a novel deep ...
Multi-camera Live Video Streaming over Wireless Network
Advances in Mobile Computing and Multimedia IntelligenceAbstractDue to the development of wireless communication technology, more and more streamers are using cameras mounted on mobile devices for live streaming in a wireless LAN environment. Conventional live streaming systems, which employ multiple images ...
Comments