Stvarno-vremenska procjena iskustvene kvalitete usluge YouTube temeljena na strojnom učenju i neovisna o postojanju korisničkih interakcija

master's thesis

• Title (english): Machine Learning-Based User-Interaction-Agnostic Real-Time YouTube Quality of Experience Estimation
• Author: Blaž Planinić
• Mentor: Lea Skorin-Kapov (mentor)
• Committee member: Lea Skorin-Kapov (predsjednik povjerenstva)
• Committee member: Mirko Sužnjević (član povjerenstva)
• Committee member: Maja Matijašević (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-07-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Gledanje videa na zahtjev je jedna od najpopularnijih aktivnosti na Internetu danas. Iz tog razloga, davateljima mrežnih usluga u cilju je pružiti što bolju uslugu krajnjim korisnicima, a da pritom efikasno upravljaju ograničenim mrežnim resursima. Podaci koji utječu na iskustvenu kvalitetu korisnika su u mreži enkriptirani te davatelji mrežnih usluga nemaju uvid u njih. Rješenje za praćenje iskustvene kvalitete pronalazi se u metodama strojnog učenja. Međutim, korisničko ponašanje prilikom gledanja videozapisa u obliku interakcija utječe na uzorke u mrežnom prometu pa samim time i na procjenu iskustvene kvalitete. Stoga, u ovom radu je ispitana mogućnost definiranja mrežnih značajki koje ne ovise u postojanju korisničkih interakcija prilikom gledanja videozapisa na YouTube-u, te izgradnje modela za procjenu iskustvene kvalitete koji adresiraju slučajeve s i bez korisničkih interakcijama.

Abstract (english)

Nowadays, video on demand (VOD) streaming is one of the most popular activities on the Internet. Therefore, a challenge faced by network operators is to fulfill customer demands and expectations while efficiently managing their limited network resources. Data affecting the Quality of Experience (QoE) are encrypted, so network operators do not have direct insight into them. Solutions which include machine learning methods in predicting QoE based on the analysis of encrypted network traffic are promising. However, customer interactions while watching VoD affect network traffic, and consequently QoE estimation. Hence, this thesis explores possibilities of defining network features which do not depend on customer interactions while watching YouTube videos, and training models for QoE estimation that include data which may and may not depend on customer interactions.

• Keywords: YouTube; iskustvena kvaliteta; video na zahtjev; strojno učenje
• Keywords (english): YouTube; Quality of Experience; video on demand; machine learning
• Language: croatian
• URN:NBN: urn:nbn:hr:168:517639
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 13:30:08