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research-article

Opportunistic Transmission for Video Streaming over Wild Internet

Published: 11 February 2023 Publication History

Abstract

The video streaming system employs adaptive bitrate (ABR) algorithms to optimize a user’s quality of experience. However, it is hard for ABR algorithms to choose the right bitrate consistently under highly dynamic bandwidth fluctuations in wild Internet. In this article, we propose a building block on the client side named Opportunistic Chunk Replacement Mechanism (OCRM) to help existing ABR algorithms make full use of the available bandwidth to improve the network utilization and viewing experience of users. Specifically, the servers take advantages of the spare bandwidth to opportunistically transmit high-quality chunks (called opportunistic chunks) with low priority to the client, without incurring any extra delay. Then, the client player replaces the low-quality chunks with the opportunistic ones that have high quality. We compare OCRM with state-of-the-art ABR algorithms by using trace-driven experiments spanning a wide variety of quality of experience metrics and network conditions. The test results show that OCRM effectively achieves high network utilization and improves the user’s viewing experience by up to 35%.

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  • (2024)VASE: Enhancing Adaptive Bitrate Selection for VBR-Encoded Audio and Video Content With Deep Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.344837023:12(14889-14902)Online publication date: Dec-2024
  • (2024)TSBG: A Two-Stage Stackelberg Game Algorithm for QoE-Awareness Video Streaming TransmissionIEEE Transactions on Mobile Computing10.1109/TMC.2024.341286023:12(12558-12571)Online publication date: Dec-2024
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    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Multimedia Computing, Communications, and Applications
    ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 3s
    October 2022
    381 pages
    ISSN:1551-6857
    EISSN:1551-6865
    DOI:10.1145/3567476
    • Editor:
    • Abdulmotaleb El Saddik
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 February 2023
    Online AM: 12 March 2022
    Accepted: 24 September 2021
    Revised: 18 August 2021
    Received: 05 April 2021
    Published in TOMM Volume 18, Issue 3s

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    Author Tags

    1. DASH
    2. video streaming
    3. ABR

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    • Research-article
    • Refereed

    Funding Sources

    • National Natural Science Foundation of China
    • Key Research and Development Program of Hunan
    • Natural Science Foundation of Hunan Province, China
    • China Postdoctoral Science Foundation

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    Cited By

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    • (2024)VASE: Enhancing Adaptive Bitrate Selection for VBR-Encoded Audio and Video Content With Deep Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.344837023:12(14889-14902)Online publication date: Dec-2024
    • (2024)TSBG: A Two-Stage Stackelberg Game Algorithm for QoE-Awareness Video Streaming TransmissionIEEE Transactions on Mobile Computing10.1109/TMC.2024.341286023:12(12558-12571)Online publication date: Dec-2024
    • (2024)Optimizing Video Streaming in Dynamic Networks: An Intelligent Adaptive Bitrate Solution Considering Scene Intricacy and Data BudgetIEEE Transactions on Mobile Computing10.1109/TMC.2024.340640923:12(12280-12297)Online publication date: Dec-2024
    • (2024)Buffer-Based Adaptive Bitrate Algorithm for Enhanced Quality of Experience2024 International Telecommunications Conference (ITC-Egypt)10.1109/ITC-Egypt61547.2024.10620520(721-726)Online publication date: 22-Jul-2024
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