DASH QoE performance evaluation framework with 5G datasets

Show simple item record

dc.contributor.author Ul Mustafa, Raza
dc.contributor.author Islam, Md. Tariqul
dc.contributor.author Rothenberg, Christian E.
dc.contributor.author Ferlin, Simone
dc.contributor.author Raca, Darijo
dc.contributor.author Quinlan, Jason J.
dc.date.accessioned 2020-10-21T10:30:57Z
dc.date.available 2020-10-21T10:30:57Z
dc.date.issued 2020-11-02
dc.identifier.citation Ul Mustafa, R., Islam,M. T., Rothenberg, C. E., Ferlin, S., Raca, D. and Quinlan, J. J. (2020) ‘DASH QoE Performance Evaluation Framework with 5G Datasets’, 2020 16th International Conference on Network and Service Management (CNSM), Izmir, Turkey [online], 2-6 Nov. doi: 10.23919/CNSM50824.2020.9269111 en
dc.identifier.startpage 1 en
dc.identifier.endpage 6 en
dc.identifier.isbn 978-3-903176-31-7
dc.identifier.uri http://hdl.handle.net/10468/10672
dc.identifier.doi 10.23919/CNSM50824.2020.9269111
dc.description.abstract Fifth Generation (5G) networks provide high throughput and low delay, contributing to enhanced Quality of Experience (QoE) expectations. The exponential growth of multimedia traffic pose dichotomic challenges to simultaneously satisfy network operators, service providers, and end-user expectations. Building QoE-aware networks that provide run-time mechanisms to satisfy end-users’ expectations while the end-to end network Quality of Service (QoS) varies is challenging and motivates many ongoing research efforts. The contribution of this work is twofold. Firstly, we present a reproducible data-driven framework with a series of pre-installed Dynamic Adaptive Streaming over HTTP (DASH) tools to analyse state of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key QoS parameters in static and mobility scenarios. Secondly, we introduce an interactive Binder notebook providing a live analytical environment which processes the output dataset of the framework and compares the relationship of five QoE models, three QoS parameters (RTT, throughput, packets), and seven video KPIs. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en
dc.relation.uri https://ieeexplore.ieee.org/document/9269111
dc.rights © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works en
dc.subject 5G en
dc.subject QoE en
dc.subject Quality of Experience (QoE) en
dc.subject Quality of Service (QoS) en
dc.subject QoS en
dc.subject ABS algorithm en
dc.subject Adaptive Bitrate Streaming (ABS) en
dc.subject DASH en
dc.subject Dynamic Adaptive Streaming over HTTP (DASH en
dc.title DASH QoE performance evaluation framework with 5G datasets en
dc.type Conference item en
dc.internal.authorcontactother Jason Quinlan, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: j.quinlan@cs.ucc.ie en
dc.internal.availability Full text available en
dc.description.version Accepted Version en
dc.description.status Peer reviewed en
dc.internal.conferencelocation Virtual Conference [Izmir, Turkey]
dc.internal.IRISemailaddress j.quinlan@cs.ucc.ie en
dc.identifier.eissn 2165-963X


Files in this item

This item appears in the following Collection(s)

Show simple item record

This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement