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Multimedia Tools and Applications

, Volume 78, Issue 23, pp 34095–34127 | Cite as

CaR-PLive: Cloud-assisted reinforcement learning based P2P live video streaming: a hybrid approach

  • Majid Sina
  • Mehdi DehghanEmail author
  • Amir Masoud Rahmani
Article
  • 42 Downloads

Abstract

In recent years, live video streaming has become one of the most popular and prevalent applications of the Internet. The Peer-to-Peer (P2P) and Content Delivery Network (CDN) are popular approaches to stream video contents. These approaches respectively have faced some drastic challenges such as obtaining the desired Quality of Service (QoS) level and minimizing economic cost. The cloud computing infrastructures can reveal proper solutions to these problems. The P2P systems can eliminate their bandwidth shortage by renting resources from the cloud environment. This paper depicts CaR-PLive as a hybrid cloud-assisted P2P live streaming system. CaR-PLive uses video servers such as Amazon EC2 from cloud to stream video contents and rents Cloud Storage Services (CSSs) such as Amazon S3 to assist P2P live streaming system to reach the desired playback continuity. In CaR-PLive, we proposed two stages (sub-windows) sliding window for buffer management that a sub-window belongs to the P2P system and another one belongs to CSS. The objective of CAR-PLive is to optimize the size of sub-windows to minimize the overall rental cost of CSS restricted to a desired QoS level. We formulate this problem as an optimization problem and model it with Markov Decision Process (MDP) and then propose a reinforcement learning based algorithm to solve this problem. Finally, we evaluate the performance of CaR-PLive by performing extensive simulations and experiments with realistic settings. Simulation results demonstrate that CaR-PLive efficiently mitigates overall CSS billing cost in different system configurations and provides desired playback continuity in different system settings.

Keywords

Cloud computing Peer-to-peer networks Live video streaming Resource provisioning Reinforcement learning 

Notes

Acknowledgements

The authors offer their gratitude to Dr. Amir H. Payberah at KTH Royal Institute of Technology, Stockholm, Sweden, Department of Software and Computer System for providing the simulation codes and experimental results of CLIVE.

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Authors and Affiliations

  1. 1.Department of Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer Engineering and Information TechnologyAmirkabir University of TechnologyTehranIran
  3. 3.Computer ScienceUniversity of Human DevelopmentSulaimanyahIraq

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