Real-world Video Adaptation with Reinforcement Learning

• URL: http://arxiv.org/abs/2008.12858v1
• Date: Fri, 28 Aug 2020 21:44:24 GMT
• Title: Real-world Video Adaptation with Reinforcement Learning
• Authors: Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy
• Abstract summary: Client-side video players employ adaptive (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. In a week-long worldwide deployment with more than 30 million video streaming sessions, our RL approach outperforms the existing human-engineered ABR algorithms.
• Score: 38.26695924173461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 existing human-engineered ABR algorithms.

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