QoE-driven video delivery improvement using packet loss prediction
The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates...
Ausführliche Beschreibung
Autor*in: |
Immich, Roger [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2015 |
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Rechteinformationen: |
Nutzungsrecht: © 2015 Taylor & Francis 2015 |
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Schlagwörter: |
unequal error protection (UEP) |
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Systematik: |
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Übergeordnetes Werk: |
Enthalten in: International journal of parallel, emergent and distributed systems - Abingdon : Taylor & Francis, 2005, 30(2015), 6, Seite 478 |
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Übergeordnetes Werk: |
volume:30 ; year:2015 ; number:6 ; pages:478 |
Links: |
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DOI / URN: |
10.1080/17445760.2015.1044004 |
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Katalog-ID: |
OLC1959686828 |
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520 | |a The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. | ||
540 | |a Nutzungsrecht: © 2015 Taylor & Francis 2015 | ||
650 | 4 | |a video-aware FEC | |
650 | 4 | |a ant colony optimization | |
650 | 4 | |a QoE | |
650 | 4 | |a motion vectors (MVs) | |
650 | 4 | |a unequal error protection (UEP) | |
650 | 4 | |a packet loss rate prediction | |
650 | 4 | |a neural networks | |
650 | 4 | |a forward error correction (FEC) | |
650 | 4 | |a Error correction & detection | |
650 | 4 | |a Quality | |
650 | 4 | |a Neural networks | |
650 | 4 | |a Wireless networks | |
700 | 1 | |a Borges, Pedro |4 oth | |
700 | 1 | |a Cerqueira, Eduardo |4 oth | |
700 | 1 | |a Curado, Marilia |4 oth | |
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10.1080/17445760.2015.1044004 doi PQ20160617 (DE-627)OLC1959686828 (DE-599)GBVOLC1959686828 (PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0 (KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr DE-627 ger DE-627 rakwb eng 004 DNB SA 5869 AVZ rvk 54.51 bkl 54.25 bkl 31.76 bkl Immich, Roger verfasserin aut QoE-driven video delivery improvement using packet loss prediction 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. Nutzungsrecht: © 2015 Taylor & Francis 2015 video-aware FEC ant colony optimization QoE motion vectors (MVs) unequal error protection (UEP) packet loss rate prediction neural networks forward error correction (FEC) Error correction & detection Quality Neural networks Wireless networks Borges, Pedro oth Cerqueira, Eduardo oth Curado, Marilia oth Enthalten in International journal of parallel, emergent and distributed systems Abingdon : Taylor & Francis, 2005 30(2015), 6, Seite 478 (DE-627)483035033 (DE-600)2183688-7 (DE-576)117278416 1744-5760 nnns volume:30 year:2015 number:6 pages:478 http://dx.doi.org/10.1080/17445760.2015.1044004 Volltext http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004 http://search.proquest.com/docview/1735889953 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4324 SA 5869 54.51 AVZ 54.25 AVZ 31.76 AVZ AR 30 2015 6 478 |
spelling |
10.1080/17445760.2015.1044004 doi PQ20160617 (DE-627)OLC1959686828 (DE-599)GBVOLC1959686828 (PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0 (KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr DE-627 ger DE-627 rakwb eng 004 DNB SA 5869 AVZ rvk 54.51 bkl 54.25 bkl 31.76 bkl Immich, Roger verfasserin aut QoE-driven video delivery improvement using packet loss prediction 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. Nutzungsrecht: © 2015 Taylor & Francis 2015 video-aware FEC ant colony optimization QoE motion vectors (MVs) unequal error protection (UEP) packet loss rate prediction neural networks forward error correction (FEC) Error correction & detection Quality Neural networks Wireless networks Borges, Pedro oth Cerqueira, Eduardo oth Curado, Marilia oth Enthalten in International journal of parallel, emergent and distributed systems Abingdon : Taylor & Francis, 2005 30(2015), 6, Seite 478 (DE-627)483035033 (DE-600)2183688-7 (DE-576)117278416 1744-5760 nnns volume:30 year:2015 number:6 pages:478 http://dx.doi.org/10.1080/17445760.2015.1044004 Volltext http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004 http://search.proquest.com/docview/1735889953 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4324 SA 5869 54.51 AVZ 54.25 AVZ 31.76 AVZ AR 30 2015 6 478 |
allfields_unstemmed |
10.1080/17445760.2015.1044004 doi PQ20160617 (DE-627)OLC1959686828 (DE-599)GBVOLC1959686828 (PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0 (KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr DE-627 ger DE-627 rakwb eng 004 DNB SA 5869 AVZ rvk 54.51 bkl 54.25 bkl 31.76 bkl Immich, Roger verfasserin aut QoE-driven video delivery improvement using packet loss prediction 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. Nutzungsrecht: © 2015 Taylor & Francis 2015 video-aware FEC ant colony optimization QoE motion vectors (MVs) unequal error protection (UEP) packet loss rate prediction neural networks forward error correction (FEC) Error correction & detection Quality Neural networks Wireless networks Borges, Pedro oth Cerqueira, Eduardo oth Curado, Marilia oth Enthalten in International journal of parallel, emergent and distributed systems Abingdon : Taylor & Francis, 2005 30(2015), 6, Seite 478 (DE-627)483035033 (DE-600)2183688-7 (DE-576)117278416 1744-5760 nnns volume:30 year:2015 number:6 pages:478 http://dx.doi.org/10.1080/17445760.2015.1044004 Volltext http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004 http://search.proquest.com/docview/1735889953 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4324 SA 5869 54.51 AVZ 54.25 AVZ 31.76 AVZ AR 30 2015 6 478 |
allfieldsGer |
10.1080/17445760.2015.1044004 doi PQ20160617 (DE-627)OLC1959686828 (DE-599)GBVOLC1959686828 (PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0 (KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr DE-627 ger DE-627 rakwb eng 004 DNB SA 5869 AVZ rvk 54.51 bkl 54.25 bkl 31.76 bkl Immich, Roger verfasserin aut QoE-driven video delivery improvement using packet loss prediction 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. Nutzungsrecht: © 2015 Taylor & Francis 2015 video-aware FEC ant colony optimization QoE motion vectors (MVs) unequal error protection (UEP) packet loss rate prediction neural networks forward error correction (FEC) Error correction & detection Quality Neural networks Wireless networks Borges, Pedro oth Cerqueira, Eduardo oth Curado, Marilia oth Enthalten in International journal of parallel, emergent and distributed systems Abingdon : Taylor & Francis, 2005 30(2015), 6, Seite 478 (DE-627)483035033 (DE-600)2183688-7 (DE-576)117278416 1744-5760 nnns volume:30 year:2015 number:6 pages:478 http://dx.doi.org/10.1080/17445760.2015.1044004 Volltext http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004 http://search.proquest.com/docview/1735889953 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4324 SA 5869 54.51 AVZ 54.25 AVZ 31.76 AVZ AR 30 2015 6 478 |
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10.1080/17445760.2015.1044004 doi PQ20160617 (DE-627)OLC1959686828 (DE-599)GBVOLC1959686828 (PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0 (KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr DE-627 ger DE-627 rakwb eng 004 DNB SA 5869 AVZ rvk 54.51 bkl 54.25 bkl 31.76 bkl Immich, Roger verfasserin aut QoE-driven video delivery improvement using packet loss prediction 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. Nutzungsrecht: © 2015 Taylor & Francis 2015 video-aware FEC ant colony optimization QoE motion vectors (MVs) unequal error protection (UEP) packet loss rate prediction neural networks forward error correction (FEC) Error correction & detection Quality Neural networks Wireless networks Borges, Pedro oth Cerqueira, Eduardo oth Curado, Marilia oth Enthalten in International journal of parallel, emergent and distributed systems Abingdon : Taylor & Francis, 2005 30(2015), 6, Seite 478 (DE-627)483035033 (DE-600)2183688-7 (DE-576)117278416 1744-5760 nnns volume:30 year:2015 number:6 pages:478 http://dx.doi.org/10.1080/17445760.2015.1044004 Volltext http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004 http://search.proquest.com/docview/1735889953 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4324 SA 5869 54.51 AVZ 54.25 AVZ 31.76 AVZ AR 30 2015 6 478 |
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004 DNB SA 5869 AVZ rvk 54.51 bkl 54.25 bkl 31.76 bkl QoE-driven video delivery improvement using packet loss prediction video-aware FEC ant colony optimization QoE motion vectors (MVs) unequal error protection (UEP) packet loss rate prediction neural networks forward error correction (FEC) Error correction & detection Quality Neural networks Wireless networks |
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ddc 004 rvk SA 5869 bkl 54.51 bkl 54.25 bkl 31.76 misc video-aware FEC misc ant colony optimization misc QoE misc motion vectors (MVs) misc unequal error protection (UEP) misc packet loss rate prediction misc neural networks misc forward error correction (FEC) misc Error correction & detection misc Quality misc Neural networks misc Wireless networks |
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ddc 004 rvk SA 5869 bkl 54.51 bkl 54.25 bkl 31.76 misc video-aware FEC misc ant colony optimization misc QoE misc motion vectors (MVs) misc unequal error protection (UEP) misc packet loss rate prediction misc neural networks misc forward error correction (FEC) misc Error correction & detection misc Quality misc Neural networks misc Wireless networks |
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QoE-driven video delivery improvement using packet loss prediction |
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The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. |
abstractGer |
The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. |
abstract_unstemmed |
The video delivery over wireless networks has risen in popularity in the recent years. However, in order to provide a high quality of experience (QoE) to the end users, it is necessary to deal with several challenges ranging from the fluctuating bandwidth and scarce resources to the high error rates. The use of these error-prone networks unveils the need for an adaptive mechanism to ensure the quality of the delivered video streams. Adaptive forward error correction (FEC) techniques with QoE assurance are desired to protect the stream, preserving the video quality. The adaptive FEC-based mechanism proposed in this article uses several video characteristics and packet loss rate prediction to shield real-time video transmission over static wireless mesh networks, improving both user experience and the usage of resources. This is possible through a combination of a random neural network, to categorise motion intensity of the videos, and an ant colony optimisation scheme, for dynamic redundancy allocation. The benefits and drawbacks are demonstrated through simulations and assessed with QoE metrics, showing that the proposed mechanism outperforms both adaptive and non-adaptive schemes. |
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QoE-driven video delivery improvement using packet loss prediction |
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Borges, Pedro Cerqueira, Eduardo Curado, Marilia |
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