A Comparative Study of DASH Representation Sets Using Real User Characteristics
C. Kreuzberger, B. Rainer, H. Hellwagner, L. Toni and P. Frossard
Published at NOSSDAV 2016 (co-located with MMSyS 2016), Klagenfurt, Austria, May 2016.
Link to Paper: PDF
BibTeX Cite: To Appear
Paper Website: http://concert.itec.aau.at/NOSSDAV_2016/
Git Repo: https://github.com/ChristianKreuzberger/amust-ndnSIM
Dataset URL: http://concert.itec.aau.at/SVCDataset/
Toolchain @ Github
BibTeX for citation
Paper Download Link: TBA (March 2015)
Abstract: With video streaming becoming more and more popular, the number of devices that are capable of streaming videos over the Internet is growing. This leads to a heterogeneous device landscape with varying demands. Dynamic Adaptive Streaming over HTTP (DASH) offers an elegant solution to these demands. Smart adaptation logics are able to adjust the clients' streaming quality according to several (local) parameters. Recent research indicated benefits of blending Scalable Video Coding (SVC) with DASH, especially considering Future Internet architectures. However, except for a DASH dataset with a single SVC encoded video, no other datasets are publicly available. The contribution of this paper is two-fold. First, a DASH/SVC dataset, containing multiple videos at varying bitrates and spatial resolutions including 1080p, is presented. Second, a toolchain for multiplexing SVC encoded videos is provided, therefore making our results reproducible and allowing researchers to generate their own datasets.
Based on our last paper, we published another paper called " Using In-Network Adaptation to Tackle Inefficiencies Caused by DASH in Information-Centric Networks". It is available here:
Our paper "Client Starvation: A Shortcoming of Client-driven Adaptive Streaming in Named Data Networking", which uses Scalable Video Coding with DASH in Information-Centric Networking got published! You can find it here:
... is a cool thing to do. Just don't stop after the first one.