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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  1/2019 - 12
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Adaptive Quality Control Scheme Based on VBR Characteristics to Improve QoE of UHD Streaming Service

KIM, M., CHUNG, K. See more information about CHUNG, K. on SCOPUS See more information about CHUNG, K. on SCOPUS See more information about CHUNG, K. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,313 KB) | Citation | Downloads: 100 | Views: 137

Author keywords
adaptive control, data transfer, high definition video, quality of service, streaming media

References keywords
streaming(16), communications(12), video(11), adaptation(11), adaptive(9), quality(7), rate(6), experience(6), bitrate(6), tutorials(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-02-28
Volume 19, Issue 1, Year 2019, On page(s): 89 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.01012
Web of Science Accession Number: 000459986900012

Abstract
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Recently, with the development of networks and smart devices, the demand for Ultra High Definition (UHD) video has risen, and HTTP adaptive streaming has attracted attention. HTTP adaptive streaming can guarantee high Quality of Experience (QoE) because it adaptively selects the video quality according to the network state. However, the existing quality control schemes experience unnecessary quality changes and low average video quality due to the bandwidth measurement and the quality control that do not consider the Variable Bit Rate (VBR) content characteristics of the UHD video. In this paper, we propose an adaptive quality control scheme based on VBR content characteristics to improve QoE of UHD streaming service. The proposed scheme measures the bandwidth using the actual bit rate of the segment and the difference in the network adaptability between segments. Furthermore, the proposed scheme defines a quality control region by considering the buffer state of the client. The quality control region consists of four subregions based on buffer thresholds, and the client selects the quality differently according to each subregion. Experimental results have shown that the proposed scheme improves the QoE compared to the existing schemes by minimizing the unnecessary quality changes and maximizing the average video quality.


References | Cited By  «-- Click to see who has cited this paper

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[2] J. Kua, G. Armitage, and P. Branch, "A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming over HTTP," IEEE Communications Surveys & Tutorials, Vol. 19, No. 3, pp. 1842-1866, Mar. 2017.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 44]


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[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef] [SCOPUS Times Cited 3]


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[18] H. Nam, K. H. Kim, and H. Schulzrinne, "QoE Matters More Than QoS: Why People Stop Watching Cat Videos," Proc. of the IEEE International Conference on Computer Communications, pp. 1-9, Apr. 2016.
[CrossRef] [SCOPUS Times Cited 34]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 1]


[29] The Network Simulator NS-3, [Online] Available: Temporary on-line reference link removed - see the PDF document



References Weight

Web of Science® Citations for all references: 578 TCR
SCOPUS® Citations for all references: 1,917 TCR

Web of Science® Average Citations per reference: 19 ACR
SCOPUS® Average Citations per reference: 64 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-04-16 09:44 in 155 seconds.




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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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