Journal
SIGCOMM '17: PROCEEDINGS OF THE 2017 CONFERENCE OF THE ACM SPECIAL INTEREST GROUP ON DATA COMMUNICATION
Volume -, Issue -, Pages 197-210Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3098822.3098843
Keywords
bitrate adaptation; video streaming; reinforcement learning
Categories
Funding
- NSF [CNS-1617702, CNS-1563826, CNS-1407470]
- MIT Center for Wireless Networks and Mobile Computing
- Qualcomm Innovation Fellowship
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Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). Despite the abundance of recently proposed schemes, state-of-the-art ABR algorithms suffer from a key limitation: they use fixed control rules based on simplified or inaccurate models of the deployment environment. As a result, existing schemes inevitably fail to achieve optimal performance across a broad set of network conditions and QoE objectives. We propose Pensieve, a system that generates ABR algorithms using reinforcement learning (RL). Pensieve trains a neural network model that selects bitrates for future video chunks based on observations collected by client video players. Pensieve does not rely on pre-programmed models or assumptions about the environment. Instead, it learns to make ABR decisions solely through observations of the resulting performance of past decisions. As a result, Pensieve automatically learns ABR algorithms that adapt to a wide range of environments and QoE metrics. We compare Pensieve to state-of-theart ABR algorithms using trace-driven and real world experiments spanning a wide variety of network conditions, QoE metrics, and video properties. In all considered scenarios, Pensieve outperforms the best state-of-the-art scheme, with improvements in average QoE of 12%-25%. Pensieve also generalizes well, outperforming existing schemes even on networks for which it was not explicitly trained.
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