QoE Enhancement using SVC and Throughput Prediction
A Master of Science thesis in Electrical Engineering by Tarnim Nos entitled, “QoE Enhancement using SVC and Throughput Prediction”, submitted in September 2025. Thesis advisor is Dr. Mahmoud H. Ismail Ibrahim and thesis co-advisors are Dr. Mohamed Hassan and Dr. Taha Landolsi. Soft copy is available...
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| Format: | doctoralThesis |
| Published: |
2025
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| Online Access: | https://hdl.handle.net/11073/33506 |
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| _version_ | 1870676404158332928 |
|---|---|
| author | Nos, Tarnim |
| author_facet | Nos, Tarnim |
| author_role | author |
| dc.contributor.none.fl_str_mv | Ibrahim, Mahmoud Hassan, Mohamed Landolsi, Taha |
| dc.creator.none.fl_str_mv | Nos, Tarnim |
| dc.date.none.fl_str_mv | 2025-09 2026-06-18T07:11:10Z 2026-06-18T07:11:10Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2025.76 https://hdl.handle.net/11073/33506 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | Master of Science in Electrical Engineering (MSEE) |
| dc.subject.none.fl_str_mv | DASH SVC Throughput Prediction NHiTS Deep Learning SimEvents |
| dc.title.none.fl_str_mv | QoE Enhancement using SVC and Throughput Prediction |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Electrical Engineering by Tarnim Nos entitled, “QoE Enhancement using SVC and Throughput Prediction”, submitted in September 2025. Thesis advisor is Dr. Mahmoud H. Ismail Ibrahim and thesis co-advisors are Dr. Mohamed Hassan and Dr. Taha Landolsi. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_20c3cc49a92cc0372ab7df2e106decd9 |
| identifier_str_mv | 35.232-2025.76 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/33506 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | QoE Enhancement using SVC and Throughput PredictionNos, TarnimDASHSVCThroughput PredictionNHiTSDeep LearningSimEventsA Master of Science thesis in Electrical Engineering by Tarnim Nos entitled, “QoE Enhancement using SVC and Throughput Prediction”, submitted in September 2025. Thesis advisor is Dr. Mahmoud H. Ismail Ibrahim and thesis co-advisors are Dr. Mohamed Hassan and Dr. Taha Landolsi. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE)Ibrahim, MahmoudHassan, MohamedLandolsi, Taha2026-06-18T07:11:10Z2026-06-18T07:11:10Z2025-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2025.76https://hdl.handle.net/11073/33506en_USMaster of Science in Electrical Engineering (MSEE)oai:repository.aus.edu:11073/335062026-06-19T06:24:59Z |
| spellingShingle | QoE Enhancement using SVC and Throughput Prediction Nos, Tarnim DASH SVC Throughput Prediction NHiTS Deep Learning SimEvents |
| status_str | publishedVersion |
| title | QoE Enhancement using SVC and Throughput Prediction |
| title_full | QoE Enhancement using SVC and Throughput Prediction |
| title_fullStr | QoE Enhancement using SVC and Throughput Prediction |
| title_full_unstemmed | QoE Enhancement using SVC and Throughput Prediction |
| title_short | QoE Enhancement using SVC and Throughput Prediction |
| title_sort | QoE Enhancement using SVC and Throughput Prediction |
| topic | DASH SVC Throughput Prediction NHiTS Deep Learning SimEvents |
| url | https://hdl.handle.net/11073/33506 |