Buffer-Based Reinforcement Learning for Adaptive Streaming

Yue Zhang; Yao Liu

doi:10.1109/ICDCS.2017.146

Buffer-Based Reinforcement Learning for Adaptive Streaming

Yue Zhang, Yao Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

Adaptive streaming improves user-perceived quality by altering the streaming bitrate depending on network conditions, trading reduced video bitrates for reduced stall times. Existing adaptation approaches, e.g., rate-based, buffer-based, either rely heavily on accurate bandwidth prediction or can be overly-conservative about video bitrates. In this work, we propose a reinforcement learning approach to choose the segment quality during playback. This approach uses only the buffer state information and optimizes for a measure of user-perceived streaming quality. Simulation results show that our proposed approach achieves better QoE than rate-, buffer-based approaches, as well as other reinforcement learning approaches.

Original language	English (US)
Title of host publication	Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017
Editors	Kisung Lee, Ling Liu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2569-2570
Number of pages	2
ISBN (Electronic)	9781538617915
DOIs	https://doi.org/10.1109/ICDCS.2017.146
State	Published - Jul 13 2017
Externally published	Yes
Event	37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 - Atlanta, United States Duration: Jun 5 2017 → Jun 8 2017

Publication series

Name	Proceedings - International Conference on Distributed Computing Systems

Other

Other	37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017
Country/Territory	United States
City	Atlanta
Period	6/5/17 → 6/8/17

All Science Journal Classification (ASJC) codes

Software
Hardware and Architecture
Computer Networks and Communications

Access to Document

10.1109/ICDCS.2017.146

Cite this

Zhang, Y., & Liu, Y. (2017). Buffer-Based Reinforcement Learning for Adaptive Streaming. In K. Lee, & L. Liu (Eds.), Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017 (pp. 2569-2570). Article 7980236 (Proceedings - International Conference on Distributed Computing Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCS.2017.146

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title = "Buffer-Based Reinforcement Learning for Adaptive Streaming",

abstract = "Adaptive streaming improves user-perceived quality by altering the streaming bitrate depending on network conditions, trading reduced video bitrates for reduced stall times. Existing adaptation approaches, e.g., rate-based, buffer-based, either rely heavily on accurate bandwidth prediction or can be overly-conservative about video bitrates. In this work, we propose a reinforcement learning approach to choose the segment quality during playback. This approach uses only the buffer state information and optimizes for a measure of user-perceived streaming quality. Simulation results show that our proposed approach achieves better QoE than rate-, buffer-based approaches, as well as other reinforcement learning approaches.",

author = "Yue Zhang and Yao Liu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 ; Conference date: 05-06-2017 Through 08-06-2017",

year = "2017",

month = jul,

day = "13",

doi = "10.1109/ICDCS.2017.146",

language = "English (US)",

series = "Proceedings - International Conference on Distributed Computing Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Zhang, Y & Liu, Y 2017, Buffer-Based Reinforcement Learning for Adaptive Streaming. in K Lee & L Liu (eds), Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017., 7980236, Proceedings - International Conference on Distributed Computing Systems, Institute of Electrical and Electronics Engineers Inc., pp. 2569-2570, 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, Atlanta, United States, 6/5/17. https://doi.org/10.1109/ICDCS.2017.146

Buffer-Based Reinforcement Learning for Adaptive Streaming. / Zhang, Yue; Liu, Yao.
Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017. ed. / Kisung Lee; Ling Liu. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2569-2570 7980236 (Proceedings - International Conference on Distributed Computing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Zhang, Yue

AU - Liu, Yao

PY - 2017/7/13

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N2 - Adaptive streaming improves user-perceived quality by altering the streaming bitrate depending on network conditions, trading reduced video bitrates for reduced stall times. Existing adaptation approaches, e.g., rate-based, buffer-based, either rely heavily on accurate bandwidth prediction or can be overly-conservative about video bitrates. In this work, we propose a reinforcement learning approach to choose the segment quality during playback. This approach uses only the buffer state information and optimizes for a measure of user-perceived streaming quality. Simulation results show that our proposed approach achieves better QoE than rate-, buffer-based approaches, as well as other reinforcement learning approaches.

AB - Adaptive streaming improves user-perceived quality by altering the streaming bitrate depending on network conditions, trading reduced video bitrates for reduced stall times. Existing adaptation approaches, e.g., rate-based, buffer-based, either rely heavily on accurate bandwidth prediction or can be overly-conservative about video bitrates. In this work, we propose a reinforcement learning approach to choose the segment quality during playback. This approach uses only the buffer state information and optimizes for a measure of user-perceived streaming quality. Simulation results show that our proposed approach achieves better QoE than rate-, buffer-based approaches, as well as other reinforcement learning approaches.

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M3 - Conference contribution

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T3 - Proceedings - International Conference on Distributed Computing Systems

SP - 2569

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BT - Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017

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T2 - 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017

Y2 - 5 June 2017 through 8 June 2017

ER -

Zhang Y, Liu Y. Buffer-Based Reinforcement Learning for Adaptive Streaming. In Lee K, Liu L, editors, Proceedings - IEEE 37th International Conference on Distributed Computing Systems, ICDCS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2569-2570. 7980236. (Proceedings - International Conference on Distributed Computing Systems). doi: 10.1109/ICDCS.2017.146

Buffer-Based Reinforcement Learning for Adaptive Streaming

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All Science Journal Classification (ASJC) codes

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