Real-world Video Adaptation with Reinforcement LearningDownload PDF

30 Apr 2019 (modified: 04 Jun 2019)ICML 2019 Workshop RL4RealLife SubmissionReaders: Everyone
  • Abstract: Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE).We evaluate recently proposed RL-based ABR methods in Facebook's web-based video streaming platform. Real-world ABR contains several challenges that requires customized designs beyond off-the-shelf RL algorithms\,---we implement a scalable neural network architecture that supports videos with arbitrary bitrate encodings; we design a training method to cope with the variance resulting from the stochasticity in network conditions; and we leverage constrained Bayesian optimization for reward shaping in order to optimize the conflicting QoE objectives. In a week-long worldwide deployment with more than 30 million video streaming sessions, our RL approach outperforms the prior ABR algorithm.
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