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Conference Paper: Adaptive Video Streaming for Massive MIMO Networks via Novel Approximate MDP

TitleAdaptive Video Streaming for Massive MIMO Networks via Novel Approximate MDP
Authors
KeywordsStreaming media
MIMO communication
Downlink
Quality of experience
Resource management
Issue Date2020
PublisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000104
Citation
Proceedings of IEEE International Conference on Communications (ICC): Communications Enabling Shared Understanding, Virtual Conference, Dublin, Ireland, 7-11 June 2020, p. 1-7 How to Cite?
AbstractThe scheduling of downlink video streaming in a massive multiple-input-multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations. Each video consists of a sequence of segments, which can be transmitted to the requesting users with variable video bitrates. To facilitate adaptive video streaming, a number of physical-layer frames are grouped as a super frame. We formulate the adaptation of transmitted segment number, frame allocation and segment bitrate in all the super frames as an infinite-horizon Markov decision process (MDP), whose objective is a discounted measurement of the average Quality-of-Experience (QoE). A novel approximate MDP method is proposed to obtain a low-complexity scheduling policy. Specifically, a baseline policy is introduced and its asymptotic value function is derived analytically. The low-complexity scheduling policy will be obtained from one-step iteration based on the analytical expression, which becomes a performance lower bound on the derived policy. It is shown by simulations that the proposed low-complexity scheduling policy has significant performance gain over the baseline policy.
DescriptionWC13: MASSIVE MIMO III
Persistent Identifierhttp://hdl.handle.net/10722/291024
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLan, Q-
dc.contributor.authorLv, B-
dc.contributor.authorWang, R-
dc.contributor.authorGong, Y-
dc.contributor.authorHuang, K-
dc.date.accessioned2020-11-02T05:50:29Z-
dc.date.available2020-11-02T05:50:29Z-
dc.date.issued2020-
dc.identifier.citationProceedings of IEEE International Conference on Communications (ICC): Communications Enabling Shared Understanding, Virtual Conference, Dublin, Ireland, 7-11 June 2020, p. 1-7-
dc.identifier.issn1550-3607-
dc.identifier.urihttp://hdl.handle.net/10722/291024-
dc.descriptionWC13: MASSIVE MIMO III-
dc.description.abstractThe scheduling of downlink video streaming in a massive multiple-input-multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations. Each video consists of a sequence of segments, which can be transmitted to the requesting users with variable video bitrates. To facilitate adaptive video streaming, a number of physical-layer frames are grouped as a super frame. We formulate the adaptation of transmitted segment number, frame allocation and segment bitrate in all the super frames as an infinite-horizon Markov decision process (MDP), whose objective is a discounted measurement of the average Quality-of-Experience (QoE). A novel approximate MDP method is proposed to obtain a low-complexity scheduling policy. Specifically, a baseline policy is introduced and its asymptotic value function is derived analytically. The low-complexity scheduling policy will be obtained from one-step iteration based on the analytical expression, which becomes a performance lower bound on the derived policy. It is shown by simulations that the proposed low-complexity scheduling policy has significant performance gain over the baseline policy.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000104-
dc.relation.ispartofIEEE International Conference on Communications (ICC) Proceedings-
dc.rightsIEEE International Conference on Communications (ICC). Copyright © IEEE.-
dc.rights©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectStreaming media-
dc.subjectMIMO communication-
dc.subjectDownlink-
dc.subjectQuality of experience-
dc.subjectResource management-
dc.titleAdaptive Video Streaming for Massive MIMO Networks via Novel Approximate MDP-
dc.typeConference_Paper-
dc.identifier.emailHuang, K: huangkb@eee.hku.hk-
dc.identifier.authorityHuang, K=rp01875-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICC40277.2020.9148822-
dc.identifier.scopuseid_2-s2.0-85091151663-
dc.identifier.hkuros317999-
dc.identifier.spage1-
dc.identifier.epage7-
dc.publisher.placeUnited States-

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