QoE estimation-based server benchmarking for virtual video delivery platform

Lauri Koskimies; Tarik Taleb; Miloud Bagaa

doi:10.1109/ICC.2017.7996445

QoE estimation-based server benchmarking for virtual video delivery platform

Lauri Koskimies, Tarik Taleb, Miloud Bagaa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

110 Downloads (Pure)

Abstract

This paper introduces a Quality of Experience (QoE) estimation-based server benchmarking system, which can be utilized as a part of QoE-optimized resource provisioning in our envisioned virtual video delivery platform. The system has been targeted for benchmarking virtual video streaming servers, i.e., virtual server flavors deployed in a cloud environment, based on resulting QoE estimates. The paper also presents another layer to the benchmarking by showing how to optimize stream segment duration in terms of estimated QoE. The QoE estimation in the system is based on a Pseudo-Subjective Quality Assessment (PSQA) method developed for video streaming. Output of the system, i.e., QoE estimation-based benchmarks, helps to find out how different factors can affect video streaming QoE which in turn makes parameter and resource optimizations possible. Moreover, the paper presents experimental benchmarking results obtained in a cloud environment.

Original language	English
Title of host publication	2017 IEEE International Conference on Communications (ICC)
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-4673-8999-0
ISBN (Print)	978-1-4673-9000-2
DOIs	https://doi.org/10.1109/ICC.2017.7996445
Publication status	Published - May 2017
MoE publication type	A4 Article in a conference publication
Event	IEEE International Conference on Communications - Paris, France Duration: 21 May 2017 → 25 May 2017

Publication series

Name	IEEE International Conference on Communications (ICC)
Publisher	IEEE
ISSN (Electronic)	1938-1883

Conference

Conference	IEEE International Conference on Communications
Abbreviated title	ICC
Country	France
City	Paris
Period	21/05/2017 → 25/05/2017

Access to Document

10.1109/ICC.2017.7996445

ELEC-Koskimies-et-al-Qoe-Estimation-IEEE_ICCAccepted author manuscript, 171 KB

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title = "QoE estimation-based server benchmarking for virtual video delivery platform",

abstract = "This paper introduces a Quality of Experience (QoE) estimation-based server benchmarking system, which can be utilized as a part of QoE-optimized resource provisioning in our envisioned virtual video delivery platform. The system has been targeted for benchmarking virtual video streaming servers, i.e., virtual server flavors deployed in a cloud environment, based on resulting QoE estimates. The paper also presents another layer to the benchmarking by showing how to optimize stream segment duration in terms of estimated QoE. The QoE estimation in the system is based on a Pseudo-Subjective Quality Assessment (PSQA) method developed for video streaming. Output of the system, i.e., QoE estimation-based benchmarks, helps to find out how different factors can affect video streaming QoE which in turn makes parameter and resource optimizations possible. Moreover, the paper presents experimental benchmarking results obtained in a cloud environment.",

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QoE estimation-based server benchmarking for virtual video delivery platform. / Koskimies, Lauri; Taleb, Tarik ; Bagaa, Miloud.

2017 IEEE International Conference on Communications (ICC). IEEE, 2017. 7996445 ( IEEE International Conference on Communications (ICC)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AB - This paper introduces a Quality of Experience (QoE) estimation-based server benchmarking system, which can be utilized as a part of QoE-optimized resource provisioning in our envisioned virtual video delivery platform. The system has been targeted for benchmarking virtual video streaming servers, i.e., virtual server flavors deployed in a cloud environment, based on resulting QoE estimates. The paper also presents another layer to the benchmarking by showing how to optimize stream segment duration in terms of estimated QoE. The QoE estimation in the system is based on a Pseudo-Subjective Quality Assessment (PSQA) method developed for video streaming. Output of the system, i.e., QoE estimation-based benchmarks, helps to find out how different factors can affect video streaming QoE which in turn makes parameter and resource optimizations possible. Moreover, the paper presents experimental benchmarking results obtained in a cloud environment.

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