Efficient delivery of scalable video using a streaming class model

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dc.contributor.author Quinlan, Jason J.
dc.contributor.author Zahran, Ahmed H.
dc.contributor.author Sreenan, Cormac J.
dc.date.accessioned 2018-03-08T16:24:38Z
dc.date.available 2018-03-08T16:24:38Z
dc.date.issued 2018-03-08
dc.identifier.citation Quinlan, J., Zahran, A. and Sreenan, C. (2018) 'Efficient Delivery of Scalable Video Using a Streaming Class Model', Information, 9(3), 59 (56pp). doi: 10.3390/info9030059 en
dc.identifier.volume 9
dc.identifier.startpage 1
dc.identifier.endpage 56
dc.identifier.issn 2078-2489
dc.identifier.uri http://hdl.handle.net/10468/5587
dc.identifier.doi 10.3390/info9030059
dc.description.abstract When we couple the rise in video streaming with the growing number of portable devices (smart phones, tablets, laptops) we see an ever-increasing demand for high-definition video online while on the move. Wireless networks are inherently characterised by restricted shared bandwidth and relatively high error loss rates, thus presenting a challenge for the efficient delivery of high quality video. Additionally, mobile devices can support/demand a range of video resolutions and qualities. This demand for mobile streaming highlights the need for adaptive video streaming schemes that can adjust to available bandwidth and heterogeneity, and can provide a graceful changes in video quality, all while respecting viewing satisfaction. In this context the use of well-known scalable/layered media streaming techniques, commonly known as scalable video coding (SVC), is an attractive solution. SVC encodes a number of video quality levels within a single media stream. This has been shown to be an especially effective and efficient solution, but it fares badly in the presence of datagram losses. While multiple description coding (MDC) can reduce the effects of packet loss on scalable video delivery, the increased delivery cost is counter productive for constrained networks. This situation is accentuated in cases where only the lower quality level is required. In this paper, we assess these issues and propose a new approach called Streaming Classes (SC) through which we can define a key set of quality levels, each of which can be delivered in a self-contained manner. This facilitates efficient delivery, yielding reduced transmission byte-cost for devices requiring lower quality, relative to MDC and ALD (42% and 76% respective reduction for layer 2), while also maintaining high levels of consistent quality. We also illustrate how selective packetisation technique can further reduce the effects of packet loss on viewable quality by leveraging the increase in the number of frames per group of pictures (GOP), while offering a means of reducing overall error correction and by providing equality of data in every packet transmitted per GOP. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher MDPI en
dc.relation.uri https://www.mdpi.com/2078-2489/9/3/59
dc.rights © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Scalable video en
dc.subject Lossy networks en
dc.subject Layered coding en
dc.subject Error resilience en
dc.subject Layer distribution en
dc.subject Rateless coding en
dc.subject Streaming classes en
dc.title Efficient delivery of scalable video using a streaming class model en
dc.type Article (peer-reviewed) 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.internal.rssid 491187240
dc.description.status Peer reviewed en
dc.identifier.journaltitle Information en
dc.internal.IRISemailaddress j.quinlan@cs.ucc.ie
dc.internal.IRISemailaddress c.sreenan@cs.ucc.ie en
dc.identifier.articleid 59


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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Except where otherwise noted, this item's license is described as © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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