YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis
Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-300240
- Around 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours ofAround 4.9 billion Internet users worldwide watch billions of hours of online video every day. As a result, streaming is by far the predominant type of traffic in communication networks. According to Google statistics, three out of five video views come from mobile devices. Thus, in view of the continuous technological advances in end devices and increasing mobile use, datasets for mobile streaming are indispensable in research but only sparsely dealt with in literature so far. With this public dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3 G/4 G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332 GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.…
Autor(en): | Frank Loh, Florian Wamser, Fabian Poignée, Stefan Geißler, Tobias Hoßfeld |
---|---|
URN: | urn:nbn:de:bvb:20-opus-300240 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Scientific Data |
Erscheinungsjahr: | 2022 |
Band / Jahrgang: | 9 |
Heft / Ausgabe: | 1 |
Aufsatznummer: | 293 |
Originalveröffentlichung / Quelle: | Scientific Data 2022, 9(1):293. DOI: 10.1038/s41597-022-01418-y |
DOI: | https://doi.org/10.1038/s41597-022-01418-y |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Freie Schlagwort(e): | YouTube; internet traffic; mobile streaming |
Datum der Freischaltung: | 31.03.2023 |
Sammlungen: | Open-Access-Publikationsfonds / Förderzeitraum 2022 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |