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Improving the Quality of Experience in Multimedia Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving the Quality of Experience in Multimedia Systems./
Author:	Dasari, Mallesham.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	144 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28869849
ISBN:	9798790629839

Improving the Quality of Experience in Multimedia Systems.
Dasari, Mallesham.

Improving the Quality of Experience in Multimedia Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 144 p.

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2021.

This item must not be sold to any third party vendors.

The Internet has become an integral part of life dominated by multimedia content involving various web and video applications. A critical performance issue in these applications is the Quality of Experience (QoE) of end-users. Despite the intense research in the past, delivering the best possible QoE for these applications is still a challenging problem because of 1) lack of clear understanding of QoE bottlenecks, 2) single-dimensional approach to resource optimization, and 3) lack of effective methodologies to compress and transport the source content. This dissertation makes three contributions to address these challenges. First, we develop an understanding of QoE by using a large-scale user study and model the objective QoE for multimedia applications. Modeling the QoE is essential for network operators to capture the actual user experience in the wild for efficient resource provisioning. Using objective QoE models, we conduct a measurement study to find out the root cause of QoE issues across diverse applications and devices. We develop an understanding of various device-related bottlenecks (e.g., processor/memory) along with the impact of network capacity affecting different applications differently. Second, we present PARSEC, a 360-degree video streaming system. PARSEC leverages a learning-based super-resolution technique to enhance the quality of 360-degree videos on the client-side when the network condition is poor. In doing so, PARSEC exploits a trade-off between network bandwidth and client-side compute capacity to improve the video quality. PARSEC achieves up to 1.8x improvement in QoE or equivalently requires 43% less bandwidth for the same QoE when compared to state-of-the-art methods. Third, we present Swift, an adaptive video streaming system using neural video codecs. Swift introduces a deep learning-based layered coding mechanism which is well suited for video streaming under variable network conditions. Swift eliminates cross-layer compression overheads and high coding latency challenges of traditional layered coding methods, and improves video QoE by up to 70% compared to existing video streaming systems.

ISBN: 9798790629839Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

360-degree videos

Improving the Quality of Experience in Multimedia Systems.
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520 $a The Internet has become an integral part of life dominated by multimedia content involving various web and video applications. A critical performance issue in these applications is the Quality of Experience (QoE) of end-users. Despite the intense research in the past, delivering the best possible QoE for these applications is still a challenging problem because of 1) lack of clear understanding of QoE bottlenecks, 2) single-dimensional approach to resource optimization, and 3) lack of effective methodologies to compress and transport the source content. This dissertation makes three contributions to address these challenges. First, we develop an understanding of QoE by using a large-scale user study and model the objective QoE for multimedia applications. Modeling the QoE is essential for network operators to capture the actual user experience in the wild for efficient resource provisioning. Using objective QoE models, we conduct a measurement study to find out the root cause of QoE issues across diverse applications and devices. We develop an understanding of various device-related bottlenecks (e.g., processor/memory) along with the impact of network capacity affecting different applications differently. Second, we present PARSEC, a 360-degree video streaming system. PARSEC leverages a learning-based super-resolution technique to enhance the quality of 360-degree videos on the client-side when the network condition is poor. In doing so, PARSEC exploits a trade-off between network bandwidth and client-side compute capacity to improve the video quality. PARSEC achieves up to 1.8x improvement in QoE or equivalently requires 43% less bandwidth for the same QoE when compared to state-of-the-art methods. Third, we present Swift, an adaptive video streaming system using neural video codecs. Swift introduces a deep learning-based layered coding mechanism which is well suited for video streaming under variable network conditions. Swift eliminates cross-layer compression overheads and high coding latency challenges of traditional layered coding methods, and improves video QoE by up to 70% compared to existing video streaming systems.
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