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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03160naa a2200409 4500 | |
| 001 | oai:DiVA.org:ltu-76619 | |
| 003 | SwePub | |
| 008 | 191105s2019 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-766192 URI |
| 024 | 7 | a https://doi.org/10.1109/QoMEX.2019.87432812 DOI |
| 040 | a (SwePub)ltu | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a kon2 swepub-publicationtype |
| 100 | 1 | a Minovski, Dimitar,d 1990-u Luleå tekniska universitet,Datavetenskap4 aut0 (Swepub:ltu)dimmin |
| 245 | 1 0 | a Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning |
| 264 | 1 | b IEEE,c 2019 |
| 338 | a print2 rdacarrier | |
| 520 | a The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users' Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R 2 value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R 2 value) regarding the same parameters using regression. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Medieteknik0 (SwePub)102092 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Media and Communication Technology0 (SwePub)102092 hsv//eng |
| 653 | a QoE | |
| 653 | a QoS | |
| 653 | a Video | |
| 653 | a MOS | |
| 653 | a PSNR | |
| 653 | a LTE | |
| 653 | a Pervasive Mobile Computing | |
| 653 | a Distribuerade datorsystem | |
| 700 | 1 | a Åhlund, Christeru Luleå tekniska universitet,Datavetenskap4 aut0 (Swepub:ltu)christer |
| 700 | 1 | a Mitra, Karan,c Assistant Professor,d 1982-u Luleå tekniska universitet,Datavetenskap4 aut0 (Swepub:ltu)karan |
| 700 | 1 | a Johansson, Peru InfoVista Sweden AB4 aut |
| 710 | 2 | a Luleå tekniska universitetb Datavetenskap4 org |
| 773 | 0 | t 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)d : IEEEz 9781538682128 |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76619 |
| 856 | 4 8 | u https://doi.org/10.1109/QoMEX.2019.8743281 |
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