Reinforcement learning-based real-time video streaming control and on-device training research - Huanhuan ZHANG; Anfu ZHOU; Huadong MA (物联网学报)
Service platforms centered on the Internet of things and mobile Internet are in accelerating process.Hundreds of millions of end-users communicate through network real-time video services, which have become an irreplaceable core tool in human’s digital life.However, the Internet is becoming dynamic, and heterogeneous, which imposes stringent requirements on real-time video streaming control technology.Moreover, the QoE of real-time video is not satisfactory.An adaptive reinforcement learning-based video intelligent transmission algorithm was designed, which can deal with heterogeneous network environment.And then, an effective end-to-end on-device training framework was designed to decrease server overhead, and a detailed evaluation and analysis on the neural network design and structure was provided.Experimental results show that the proposed algorithm can effectively predict heterogeneous network bandwidth, and reduces the bandwidth prediction error by 48.48%, comparing with the representative streaming control algorithm.The effective bandwidth prediction can further improve the user QoE, such as improving the video fluency by 60.65%, and improving the video quality by 16.52%.Besides, the analysis can provide empirical insights for further study, and holds potential to push the development of intelligent video applications.