Pregled bibliografske jedinice broj: 1014964
In-Network YouTube Performance Estimation in Light of End User Playback-Related Interactions
In-Network YouTube Performance Estimation in Light of End User Playback-Related Interactions // Proceedings of the 11th International Conference on Quality of Multimedia Experience
Berlin, Njemačka, 2019. str. 1-3 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
In-Network YouTube Performance Estimation in Light of End User Playback-Related Interactions
Autori
Bartolec, Ivan ; Oršolić, Irena ; Skorin-Kapov, Lea
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 11th International Conference on Quality of Multimedia Experience
/ - , 2019, 1-3
Skup
11th International Conference on Quality of Multimedia Experience
Mjesto i datum
Berlin, Njemačka, 05.06.2019. - 07.06.2019
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
QoE ; performance estimation ; machine learning ; YouTube ; user interactions
Sažetak
Recent research efforts have addressed the challenge of estimating HTTP adaptive video streaming Quality of Experience (QoE) and Key Performance Indicators (KPIs) from a network provider perspective, commonly relying on machine learning models and the analysis of features extracted solely from encrypted network traffic. This challenge is further complicated in light of realistic end user playback-related interactions, such as video skipping, pausing, and seeking. Given that user interactions impact traffic characteristics, such scenarios need to be considered when training QoE/KPI estimation models. We train models on datasets with and without user interactions (focusing on YouTube as a case study), with the aim to investigate the impact of user interaction on classification accuracy. Results motivate the need to systematically include data corresponding to various interaction scenarios when training QoE/KPI classification models that would be applicable in real-world scenarios.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb