The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, not withstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming

Author:Yassin Alkhalili, Jannis Weil, Anam Tahir, Tobias Meuser, Boris Koldehofe, Andreas Mauthe, Heinz Koeppl and Ralf Steinmetz
Date:September 2021
Kind:In proceedings - use for conference & workshop papers
Research Area(s):Communication Services
Abstract:Whereas adaptive video streaming for 2D video is well established and frequently used in streaming services, adaptation for emerging higher-dimensional content, such as point clouds, is still a research issue. Moreover, how to optimize resource usage in streaming services that support multiple content types of different dimensions and level of interactivity has so far not been sufficiently studied. Learning-based approaches aim to optimize the streaming experience according to user needs. They predict quality metrics and try to find system parameters maximizing them given the current network conditions. With this paper, we show how to approach content and network adaption driven by Quality of Experience (QoE) for multi-dimensional content. We describe components required to create a system adapting multiple streams of different content types simultaneously, identify research gaps and propose potential next steps.
Full paper (pdf)

[Export this entry to BibTeX]