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An open software for bitstream-based quality prediction in adaptive video streaming

Authors:

Huyen T. T. Tran,

Truong Cong ThangAuthors Info & Claims

MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference

Pages 225 - 230

https://doi.org/10.1145/3339825.3394925

Published: 27 May 2020 Publication History

Abstract

HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. However, because of throughput fluctuations, video quality may be dramatically varying. Also, stalling events may occur during a streaming session, causing negative impacts on user experience. Therefore, a main challenge in HAS is how to evaluate the overall quality of a session taking into account the impacts of quality variations and stalling events. In this paper, we present an open software, called BiQPS, using a Long-Short Term Memory (LSTM) network to predict the overall quality of HAS sessions. The prediction is based on bitstream-level parameters, so it can be directly applied in practice. Through experiment results, it is found that BiQPS outperforms four existing models. Our software has been made available to the public at https://github.com/TranHuyen1191/BiQPS.

References

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Digital Library

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Cited By

Hodžić KCosovic MMrdovic SQuinlan JRaca D(2024)DashReStreamer: Framework for Creation of Impaired Video Clips under Realistic Network ConditionsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364001621:1(1-26)Online publication date: 16-Dec-2024
https://dl.acm.org/doi/10.1145/3640016
Margetis GTsagkatakis GStamou SStephanidis C(2023)Integrating Visual and Network Data with Deep Learning for Streaming Video Quality AssessmentSensors10.3390/s2308399823:8(3998)Online publication date: 14-Apr-2023
https://doi.org/10.3390/s23083998
Luu NOnuoha CThang T(2023)Blind Image Quality Assessment With Multimodal Prompt Learning2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN59264.2023.10402183(614-618)Online publication date: 22-Dec-2023
https://doi.org/10.1109/CICN59264.2023.10402183
Show More Cited By

Index Terms

An open software for bitstream-based quality prediction in adaptive video streaming
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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Published In

cover image ACM Conferences

MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference

May 2020

403 pages

ISBN:9781450368452

DOI:10.1145/3339825

General Chairs:
Laura Toni
University College London
,
Ali C. Begen
Ozyegin University and Networked Media
,
Program Chairs:
Özgü Alay
University of Oslo and Simula Metropolitan
,
Christian Timmerer
Alpen-Adria-Universität Klagenfurt and Bitmovin

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 27 May 2020

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MMSys '20: 11th ACM Multimedia Systems Conference

June 8 - 11, 2020

Istanbul, Turkey

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MMSys '20 Paper Acceptance Rate 18 of 55 submissions, 33%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Hodžić KCosovic MMrdovic SQuinlan JRaca D(2024)DashReStreamer: Framework for Creation of Impaired Video Clips under Realistic Network ConditionsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364001621:1(1-26)Online publication date: 16-Dec-2024
https://dl.acm.org/doi/10.1145/3640016
Margetis GTsagkatakis GStamou SStephanidis C(2023)Integrating Visual and Network Data with Deep Learning for Streaming Video Quality AssessmentSensors10.3390/s2308399823:8(3998)Online publication date: 14-Apr-2023
https://doi.org/10.3390/s23083998
Luu NOnuoha CThang T(2023)Blind Image Quality Assessment With Multimodal Prompt Learning2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN59264.2023.10402183(614-618)Online publication date: 22-Dec-2023
https://doi.org/10.1109/CICN59264.2023.10402183
Thomos NMaugey TToni L(2022)Machine Learning for Multimedia CommunicationsSensors10.3390/s2203081922:3(819)Online publication date: 21-Jan-2022
https://doi.org/10.3390/s22030819
Nguyen DPham Ngoc NThang T(2022)QoE Models for Adaptive Streaming: A Comprehensive EvaluationFuture Internet10.3390/fi1405015114:5(151)Online publication date: 13-May-2022
https://doi.org/10.3390/fi14050151
Thang TWatanobe YKiran RPaik I(2022)Towards QoE Management for Post-Pandemic Online Learning : Invited Paper2022 14th International Conference on Knowledge and Systems Engineering (KSE)10.1109/KSE56063.2022.9953769(1-6)Online publication date: 19-Oct-2022
https://doi.org/10.1109/KSE56063.2022.9953769
Çetinkaya ENguyen MTimmerer C(2022)MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural NetworksMultiMedia Modeling10.1007/978-3-030-98355-0_40(465-472)Online publication date: 15-Mar-2022
https://doi.org/10.1007/978-3-030-98355-0_40
Taraghi BNguyen MAmirpour HTimmerer C(2021)Intense: In-Depth Studies on Stall Events and Quality Switches and Their Impact on the Quality of Experience in HTTP Adaptive StreamingIEEE Access10.1109/ACCESS.2021.31076199(118087-118098)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3107619

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