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Oliver Hohlfeld

Researcher at Brandenburg University of Technology

Publications -  118
Citations -  2535

Oliver Hohlfeld is an academic researcher from Brandenburg University of Technology. The author has contributed to research in topics: The Internet & QUIC. The author has an hindex of 22, co-authored 118 publications receiving 1905 citations. Previous affiliations of Oliver Hohlfeld include Telekom Innovation Laboratories & Technische Universität Darmstadt.

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The Gilbert-Elliott Model for Packet Loss in Real Time Services on the Internet

TL;DR: This work focuses on the classical Gilbert-Elliott model whose second order statistics is derived over arbitrary time scales and used to fit packet loss processes of traffic traces measured in the IP backbone of Deutsche Telekom.
Proceedings ArticleDOI

Impact of frame rate and resolution on objective QoE metrics

TL;DR: This paper introduces a framework for QoE management for video streaming systems based on the H.264/SVC codec, and quantified the influence of i) video resolution, ii) scaling method, iii) video frame rate and iv) video content types on theQoE by means of the SSIM and VQM full-reference metrics.
Proceedings ArticleDOI

Annoyed Users: Ads and Ad-Block Usage in the Wild

TL;DR: This work shows how to leverage the functionality of AdBlock Plus, one of the most popular ad-blockers to identify ad traffic from passive network measurements, and characterizes ad-traffic in the wild, i.e., as seen in a residential broadband network of a major European ISP.
Book ChapterDOI

A Quantitative Analysis of the Impact of Arbitrary Blockchain Content on Bitcoin

TL;DR: It is shown that certain content can render the mere possession of a blockchain illegal, e.g., illegal pornography, and the importance for future blockchain designs to address the possibility of unintended data insertion and protect blockchain users accordingly is highlighted.
Proceedings ArticleDOI

A Long Way to the Top: Significance, Structure, and Stability of Internet Top Lists

TL;DR: It is found that top lists generally overestimate results compared to the general population by a significant margin, often even an order of magnitude, and some top lists have surprising change characteristics, causing high day-to-day fluctuation and leading to result instability.