research-article

Delay-Aware Quality Optimization in Cloud-Assisted Video Streaming System

Authors:

Junliang ChenAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 14, Issue 1

Article No.: 4, Pages 1 - 25

https://doi.org/10.1145/3152116

Published: 13 December 2017 Publication History

Abstract

Cloud-assisted video streaming has emerged as a new paradigm to optimize multimedia content distribution over the Internet. This article investigates the problem of streaming cloud-assisted real-time video to multiple destinations (e.g., cloud video conferencing, multi-player cloud gaming, etc.) over lossy communication networks. The user diversity and network dynamics result in the delay differences among multiple destinations. This research proposes <underline>D</underline>ifferentiated cloud-<underline>A</underline>ssisted <underline>VI</underline>deo <underline>S</underline>treaming (DAVIS) framework, which proactively leverages such delay differences in video coding and transmission optimization. First, we analytically formulate the optimization problem of joint coding and transmission to maximize received video quality. Second, we develop a quality optimization framework that integrates the video representation selection and FEC (Forward Error Correction) packet interleaving. The proposed DAVIS is able to effectively perform differentiated quality optimization for multiple destinations by taking advantage of the delay differences in cloud-assisted video streaming system. We conduct the performance evaluation through extensive experiments with the Amazon EC2 instances and Exata emulation platform. Evaluation results show that DAVIS outperforms the reference cloud-assisted streaming solutions in video quality and delay performance.

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Index Terms

Delay-Aware Quality Optimization in Cloud-Assisted Video Streaming System
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 14, Issue 1

February 2018

287 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3173554

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2017 ACM.

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Association for Computing Machinery

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Publication History

Published: 13 December 2017

Accepted: 01 September 2017

Revised: 01 May 2017

Received: 01 October 2016

Published in TOMM Volume 14, Issue 1

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National Natural Science Foundation of China
National High-tech R8D Program of China (863 Program)
Natural Science Foundation of Jiangsu Province of China

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Cited By

Moina-Rivera WGarcia-Pineda MGutiérrez-Aguado JAlcaraz-Calero J(2024)Cloud media video encoding: review and challengesMultimedia Tools and Applications10.1007/s11042-024-18763-283:34(81231-81278)Online publication date: 9-Mar-2024
https://doi.org/10.1007/s11042-024-18763-2
Wang JMou LMa LHuang TGao W(2023)AMSA: Adaptive Multimodal Learning for Sentiment AnalysisACM Transactions on Multimedia Computing, Communications, and Applications10.1145/357291519:3s(1-21)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3572915
Zhang YPan YYao THuang RMei TChen C(2023)Boosting Scene Graph Generation with Visual Relation SaliencyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/351404119:1(1-17)Online publication date: 5-Jan-2023
https://dl.acm.org/doi/10.1145/3514041
Murty CVarma GChakravarthy A. S. N. (2022)Content-Based Collaborative Filtering With Predictive Error Reduction-Based CNN Using IPU ModelInternational Journal of Information Security and Privacy10.4018/IJISP.30830916:2(1-19)Online publication date: 16-Sep-2022
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