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Modeling and Analysis of Power Consumption in Live Video Streaming Systems

Authors:

Yousef O. Sharrab,

Nabil J. SarhanAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 13, Issue 4

Article No.: 54, Pages 1 - 25

https://doi.org/10.1145/3115505

Published: 18 September 2017 Publication History

Abstract

This article develops an aggregate power consumption model for live video streaming systems, including many-to-many systems. In many-to-one streaming systems, multiple video sources (i.e., cameras and/or sensors) stream videos to a monitoring station. We model the power consumed by the video sources in the capturing, encoding, and transmission phases and then provide an overall model in terms of the main capturing and encoding parameters, including resolution, frame rate, number of reference frames, motion estimation range, and quantization. We also analyze the power consumed by the monitoring station due to receiving, decoding, and upscaling the received video streams. In addition to modeling the power consumption, we model the achieved bitrate of video encoding. We validate the developed models through extensive experiments using two types of systems and different video contents. Furthermore, we analyze many-to-one systems in terms of bitrate, video quality, and the power consumed by the sources, as well as that by the monitoring station, considering the impacts of multiple parameters simultaneously.

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Cited By

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https://doi.org/10.1007/s11042-024-18666-2
Alsmirat MSharrab YTarawneh MAl-shboul SSarhan N(2023)Video coding deep learning-based modeling for long life video streaming over next network generationCluster Computing10.1007/s10586-022-03948-x26:2(1159-1167)Online publication date: 3-Jan-2023
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Bisio IHaleem HGaribotto CLavagetto FSciarrone A(2022)Innovative Flying Strategy based on Drone Energy Profile: an Application for Traffic MonitoringGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10001318(5892-5898)Online publication date: 4-Dec-2022
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Modeling and Analysis of Power Consumption in Live Video Streaming Systems

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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 13, Issue 4

November 2017

362 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3129737

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2017 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 ACM 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

New York, NY, United States

Publication History

Published: 18 September 2017

Accepted: 01 June 2017

Revised: 01 June 2017

Received: 01 September 2016

Published in TOMM Volume 13, Issue 4

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Cited By

Sharrab YAlsmirat MEljinini MSarhan N(2024)iHELP: a model for instant learning of video coding in VR/AR real-time applicationsMultimedia Tools and Applications10.1007/s11042-024-18666-283:33(79397-79436)Online publication date: 5-Mar-2024
https://doi.org/10.1007/s11042-024-18666-2
Alsmirat MSharrab YTarawneh MAl-shboul SSarhan N(2023)Video coding deep learning-based modeling for long life video streaming over next network generationCluster Computing10.1007/s10586-022-03948-x26:2(1159-1167)Online publication date: 3-Jan-2023
https://dl.acm.org/doi/10.1007/s10586-022-03948-x
Bisio IHaleem HGaribotto CLavagetto FSciarrone A(2022)Innovative Flying Strategy based on Drone Energy Profile: an Application for Traffic MonitoringGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10001318(5892-5898)Online publication date: 4-Dec-2022
https://doi.org/10.1109/GLOBECOM48099.2022.10001318
Bisio IHaleem HGaribotto CLavagetto FSciarrone A(2022)Accuracy-Versus-Energy Evaluation In Drone-Based Video Processing For Object DetectionGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10000961(5886-5891)Online publication date: 4-Dec-2022
https://doi.org/10.1109/GLOBECOM48099.2022.10000961
Sharrab YAlsmadi ISarhan N(2022)Towards the availability of video communication in artificial intelligence-based computer vision systems utilizing a multi-objective functionCluster Computing10.1007/s10586-021-03391-425:1(231-247)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10586-021-03391-4
Sharrab YAl-shboul SAlsmira MKhalifeh ADwekat ZAlsmadi IAl-Khasawneh A(2021)Performance Comparison of Several Deep Learning-Based Object Detection Algorithms Utilizing Thermal Images2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA53674.2021.9660820(16-22)Online publication date: 15-Nov-2021
https://doi.org/10.1109/IDSTA53674.2021.9660820
Wang MLi D(2020)What motivates audience comments on live streaming platforms?PLOS ONE10.1371/journal.pone.023125515:4(e0231255)Online publication date: 9-Apr-2020
https://doi.org/10.1371/journal.pone.0231255

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