Literature DB >> 35697714

YouTube Dataset on Mobile Streaming for Internet Traffic Modeling and Streaming Analysis.

Frank Loh1, Florian Wamser2, Fabian Poignée2, Stefan Geißler2, Tobias Hoßfeld2.   

Abstract

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 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.
© 2022. The Author(s).

Entities:  

Year:  2022        PMID: 35697714     DOI: 10.1038/s41597-022-01418-y

Source DB:  PubMed          Journal:  Sci Data        ISSN: 2052-4463            Impact factor:   6.444


  1 in total

1.  Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming.

Authors:  Frank Loh; Fabian Poignée; Florian Wamser; Ferdinand Leidinger; Tobias Hoßfeld
Journal:  Sensors (Basel)       Date:  2021-06-17       Impact factor: 3.576

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.