Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Monitoring of Video Streaming Quality from Encrypted Network Traffic: The Case of YouTube Streaming
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kommunikationssystem.
2016 (Engelska)Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
Abstract [en]

The video streaming applications contribute to a major share of the Internet traffic. Consequently, monitoring and management of video streaming quality has gained a significant importance in the recent years. The disturbances in the video, such as, amount of buffering and bitrate adaptations affect user Quality of Experience (QoE). Network operators usually monitor such events from network traffic with the help of Deep Packet Inspection (DPI). However, it is becoming difficult to monitor such events due to the traffic encryption. To address this challenge, this thesis work makes two key contributions. First, it presents a test-bed, which performs automated video streaming tests under controlled time-varying network conditions and measures performance at network and application level. Second, it develops and evaluates machine learning models for the detection of video buffering and bitrate adaptation events, which rely on the information extracted from packets headers. The findings of this work suggest that buffering and bitrate adaptation events within 60 second intervals can be detected using Random Forest model with an accuracy of about 70%. Moreover, the results show that the features based on time-varying patterns of downlink throughput and packet inter-arrival times play a distinctive role in the detection of such events.

Ort, förlag, år, upplaga, sidor
2016. , s. 64
Nyckelord [en]
Quality of Experience, Machine Learning, Encrypted video traffic classification, Video Streaming Quality
Nationell ämneskategori
Telekommunikation
Identifikatorer
URN: urn:nbn:se:bth-13336OAI: oai:DiVA.org:bth-13336DiVA, id: diva2:1044774
Externt samarbete
Ericsson
Ämne / kurs
ET2580 Masterarbete i elektroteknik med inriktning mot telekommunikationssystem
Utbildningsprogram
ETATE Masterprogram i Elektroteknik med inriktning mot Telekommunikation
Handledare
Examinatorer
Tillgänglig från: 2016-11-15 Skapad: 2016-11-06 Senast uppdaterad: 2016-11-15Bibliografiskt granskad

Open Access i DiVA

fulltext(2643 kB)484 nedladdningar
Filinformation
Filnamn FULLTEXT02.pdfFilstorlek 2643 kBChecksumma SHA-512
6db70610ea441be3642277b90e4436ed4965a0dac43fdf7063f7edbd7da63c01d506360f8678c3838f1f345fafb86bbb5b91aa5112f5710e44b6ed44faaaffe4
Typ fulltextMimetyp application/pdf

Av organisationen
Institutionen för kommunikationssystem
Telekommunikation

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 484 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 7542 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf