Title
Design and evaluation of tile selection algorithms for tiled HTTP adaptive streaming
Author
Devloo, J.
Lamot, N.
van Campen, J.
Weymaere, E.
Latré, S.
Famaey, J.
van Brandenburg, R.
de Turck, F.
Contributor
Doyen, G. (editor)
Waldburger, M. (editor)
Publication year
2013
Abstract
The future of digital video is envisioned to have an increase in both resolution and interactivity. New resolutions like 8k UHDTV are up to 16 times as big in number of pixels compared to current HD video. Interactivity includes the possibility to zoom and pan around in video. We examine Tiled HTTP Adaptive Streaming (TAS) as a technique for supporting these trends and allowing them to be implemented on conventional Internet infrastructure. In this article, we propose three tile selection algorithms, for different use cases (e.g., zooming, panning). A performance evaluation of these algorithms on a TAS testbed, shows that they lead to better bandwidth utilization, higher static Region of Interest (ROI) video quality and higher video quality while manipulating the ROI. We show that we can transmit video at resolutions up to four times larger than existing algorithms during bandwidth drops, which results in a higher quality viewing experience. We can also increase the video quality by up to 40 percent in interactive video, during panning or zooming. © 2013 IFIP International Federation for Information Processing.
Subject
Communication & Information
MNS - Media & Network Services
TS - Technical Sciences
Infostructures
Informatics
Information Society
Client quality selection algorithms
HTTP Adaptive Streaming
Tiled HTTP Adaptive Streaming
To reference this document use:
http://resolver.tudelft.nl/uuid:46a5490d-792e-438e-b805-a67e35cd587d
DOI
https://doi.org/10.1007/978-3-642-38998-6_3
TNO identifier
474972
Publisher
Springer, Berlin
Source
Emerging Management Mechanisms for the Future Internet. 7th IFIP WG 6.6 International Conference on Autonomous Infrastructure, Management, and Security, AIMS 2013, 25-28 June 2013, Barcelona, Spain, 25-36
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Document type
bookPart