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
Gespeichert in:
| Autor*in: |
Immich, Roger [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
|---|
| Rechteinformationen: |
Nutzungsrecht: © 2015 Taylor & Francis 2015 |
|---|
| Schlagwörter: |
unequal error protection (UEP) |
|---|
| Systematik: |
|
|---|
| Übergeordnetes Werk: |
Enthalten in: International journal of parallel, emergent and distributed systems - Abingdon : Taylor & Francis, 2005, 30(2015), 6, Seite 478 |
|---|---|
| Übergeordnetes Werk: |
volume:30 ; year:2015 ; number:6 ; pages:478 |
| Links: |
|---|
| DOI / URN: |
10.1080/17445760.2015.1044004 |
|---|
| Katalog-ID: |
OLC1959686828 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1959686828 | ||
| 003 | DE-627 | ||
| 005 | 20220214171714.0 | ||
| 007 | tu | ||
| 008 | 160206s2015 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1080/17445760.2015.1044004 |2 doi | |
| 028 | 5 | 2 | |a PQ20160617 |
| 035 | |a (DE-627)OLC1959686828 | ||
| 035 | |a (DE-599)GBVOLC1959686828 | ||
| 035 | |a (PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0 | ||
| 035 | |a (KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q DNB |
| 084 | |a SA 5869 |q AVZ |2 rvk | ||
| 084 | |a 54.51 |2 bkl | ||
| 084 | |a 54.25 |2 bkl | ||
| 084 | |a 31.76 |2 bkl | ||
| 100 | 1 | |a Immich, Roger |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a QoE-driven video delivery improvement using packet loss prediction |
| 264 | 1 | |c 2015 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 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 | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of parallel, emergent and distributed systems |d Abingdon : Taylor & Francis, 2005 |g 30(2015), 6, Seite 478 |w (DE-627)483035033 |w (DE-600)2183688-7 |w (DE-576)117278416 |x 1744-5760 |7 nnns |
| 773 | 1 | 8 | |g volume:30 |g year:2015 |g number:6 |g pages:478 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1080/17445760.2015.1044004 |3 Volltext |
| 856 | 4 | 2 | |u http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004 |
| 856 | 4 | 2 | |u http://search.proquest.com/docview/1735889953 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_4324 | ||
| 936 | r | v | |a SA 5869 |
| 936 | b | k | |a 54.51 |q AVZ |
| 936 | b | k | |a 54.25 |q AVZ |
| 936 | b | k | |a 31.76 |q AVZ |
| 951 | |a AR | ||
| 952 | |d 30 |j 2015 |e 6 |h 478 | ||
| author_variant |
r i ri |
|---|---|
| matchkey_str |
article:17445760:2015----::odievdoeieymrvmnuigak |
| hierarchy_sort_str |
2015 |
| bklnumber |
54.51 54.25 31.76 |
| publishDate |
2015 |
| allfields |
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 |
| allfieldsSound |
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 |
| language |
English |
| source |
Enthalten in International journal of parallel, emergent and distributed systems 30(2015), 6, Seite 478 volume:30 year:2015 number:6 pages:478 |
| sourceStr |
Enthalten in International journal of parallel, emergent and distributed systems 30(2015), 6, Seite 478 volume:30 year:2015 number:6 pages:478 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
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 |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
International journal of parallel, emergent and distributed systems |
| authorswithroles_txt_mv |
Immich, Roger @@aut@@ Borges, Pedro @@oth@@ Cerqueira, Eduardo @@oth@@ Curado, Marilia @@oth@@ |
| publishDateDaySort_date |
2015-01-01T00:00:00Z |
| hierarchy_top_id |
483035033 |
| dewey-sort |
14 |
| id |
OLC1959686828 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1959686828</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220214171714.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/17445760.2015.1044004</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1959686828</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1959686828</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 5869</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.51</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.25</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Immich, Roger</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">QoE-driven video delivery improvement using packet loss prediction</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2015 Taylor & Francis 2015</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">video-aware FEC</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ant colony optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">QoE</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">motion vectors (MVs)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">unequal error protection (UEP)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">packet loss rate prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">forward error correction (FEC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Error correction & detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wireless networks</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Borges, Pedro</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cerqueira, Eduardo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Curado, Marilia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of parallel, emergent and distributed systems</subfield><subfield code="d">Abingdon : Taylor & Francis, 2005</subfield><subfield code="g">30(2015), 6, Seite 478</subfield><subfield code="w">(DE-627)483035033</subfield><subfield code="w">(DE-600)2183688-7</subfield><subfield code="w">(DE-576)117278416</subfield><subfield code="x">1744-5760</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:30</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:6</subfield><subfield code="g">pages:478</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/17445760.2015.1044004</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1735889953</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 5869</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.51</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.25</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.76</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">30</subfield><subfield code="j">2015</subfield><subfield code="e">6</subfield><subfield code="h">478</subfield></datafield></record></collection>
|
| author |
Immich, Roger |
| spellingShingle |
Immich, Roger 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 QoE-driven video delivery improvement using packet loss prediction |
| authorStr |
Immich, Roger |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)483035033 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1744-5760 |
| topic_title |
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 |
| topic |
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 |
| topic_unstemmed |
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 |
| topic_browse |
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 |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
p b pb e c ec m c mc |
| hierarchy_parent_title |
International journal of parallel, emergent and distributed systems |
| hierarchy_parent_id |
483035033 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
International journal of parallel, emergent and distributed systems |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)483035033 (DE-600)2183688-7 (DE-576)117278416 |
| title |
QoE-driven video delivery improvement using packet loss prediction |
| ctrlnum |
(DE-627)OLC1959686828 (DE-599)GBVOLC1959686828 (PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0 (KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr |
| title_full |
QoE-driven video delivery improvement using packet loss prediction |
| author_sort |
Immich, Roger |
| journal |
International journal of parallel, emergent and distributed systems |
| journalStr |
International journal of parallel, emergent and distributed systems |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2015 |
| contenttype_str_mv |
txt |
| container_start_page |
478 |
| author_browse |
Immich, Roger |
| container_volume |
30 |
| class |
004 DNB SA 5869 AVZ rvk 54.51 bkl 54.25 bkl 31.76 bkl |
| format_se |
Aufsätze |
| author-letter |
Immich, Roger |
| doi_str_mv |
10.1080/17445760.2015.1044004 |
| dewey-full |
004 |
| title_sort |
qoe-driven video delivery improvement using packet loss prediction |
| title_auth |
QoE-driven video delivery improvement using packet loss prediction |
| abstract |
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. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4324 |
| container_issue |
6 |
| title_short |
QoE-driven video delivery improvement using packet loss prediction |
| url |
http://dx.doi.org/10.1080/17445760.2015.1044004 http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004 http://search.proquest.com/docview/1735889953 |
| remote_bool |
false |
| author2 |
Borges, Pedro Cerqueira, Eduardo Curado, Marilia |
| author2Str |
Borges, Pedro Cerqueira, Eduardo Curado, Marilia |
| ppnlink |
483035033 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1080/17445760.2015.1044004 |
| up_date |
2024-07-03T18:25:26.722Z |
| _version_ |
1803583358735745024 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1959686828</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220214171714.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/17445760.2015.1044004</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1959686828</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1959686828</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)i1325-506a91f2a6ef5b5d90786c8283ba0539bc2013e7bf82be5413ed4d1afa3f974f0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0229017020150000030000600478qoedrivenvideodeliveryimprovementusingpacketlosspr</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SA 5869</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.51</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.25</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Immich, Roger</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">QoE-driven video delivery improvement using packet loss prediction</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2015 Taylor & Francis 2015</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">video-aware FEC</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ant colony optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">QoE</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">motion vectors (MVs)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">unequal error protection (UEP)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">packet loss rate prediction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">forward error correction (FEC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Error correction & detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Wireless networks</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Borges, Pedro</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cerqueira, Eduardo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Curado, Marilia</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of parallel, emergent and distributed systems</subfield><subfield code="d">Abingdon : Taylor & Francis, 2005</subfield><subfield code="g">30(2015), 6, Seite 478</subfield><subfield code="w">(DE-627)483035033</subfield><subfield code="w">(DE-600)2183688-7</subfield><subfield code="w">(DE-576)117278416</subfield><subfield code="x">1744-5760</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:30</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:6</subfield><subfield code="g">pages:478</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/17445760.2015.1044004</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/17445760.2015.1044004</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1735889953</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">SA 5869</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.51</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.25</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.76</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">30</subfield><subfield code="j">2015</subfield><subfield code="e">6</subfield><subfield code="h">478</subfield></datafield></record></collection>
|
| score |
7.400339 |