@article{LohPoigneeWamseretal.2021,
  author    = {Loh, Frank and Poign{\´e}e, Fabian and Wamser, Florian and Leidinger, Ferdinand and Hoßfeld, Tobias},
  title     = {Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming},
  series = {Sensors},
  volume    = {21},
  journal   = {Sensors},
  number    = {12},
  issn      = {1424-8220},
  doi       = {10.3390/s21124172},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-241121},
  year      = {2021},
  abstract  = {Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.},
  language  = {en}
}
@article{WamserSeufertHalletal.2021,
  author    = {Wamser, Florian and Seufert, Anika and Hall, Andrew and Wunderer, Stefan and Hoßfeld, Tobias},
  title     = {Valid statements by the crowd: statistical measures for precision in crowdsourced mobile measurements},
  series = {Network},
  volume    = {1},
  journal   = {Network},
  number    = {2},
  issn      = {2673-8732},
  doi       = {10.3390/network1020013},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-284154},
  pages     = {215 -- 232},
  year      = {2021},
  abstract  = {Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user's perspective on a large scale. Here, network measurements are performed directly on the end-users' devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.},
  language  = {en}
}
@book{TranGiaHossfeld2021,
  author    = {Tran-Gia, Phuoc and Hoßfeld, Tobias},
  title     = {Performance Modeling and Analysis of Communication Networks},
  edition   = {1st edition},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-152-5},
  doi       = {10.25972/WUP-978-3-95826-153-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-241920},
  publisher      = {W{\"u}rzburg University Press},
  pages     = {xiii, 353},
  year      = {2021},
  abstract  = {This textbook provides an introduction to common methods of performance modeling and analysis of communication systems. These methods form the basis of traffic engineering, teletraffic theory, and analytical system dimensioning. The fundamentals of probability theory, stochastic processes, Markov processes, and embedded Markov chains are presented. Basic queueing models are described with applications in communication networks. Advanced methods are presented that have been frequently used in recent practice, especially discrete-time analysis algorithms, or which go beyond classical performance measures such as Quality of Experience or energy efficiency. Recent examples of modern communication networks include Software Defined Networking and the Internet of Things. Throughout the book, illustrative examples are used to provide practical experience in performance modeling and analysis. Target group: The book is aimed at students and scientists in computer science and technical computer science, operations research, electrical engineering and economics.},
  language  = {en}
}