Datasets for AVC (H.264) and HEVC (H.265) for Evaluating Dynamic Adaptive Streaming over HTTP (DASH)

Author: University College Cork

Partner: No

Contact: Jason J. Quinlan ( Ahmed H. Zahran ( Cormac J. Sreenan (




Total: 30

SRC: 6

HRC: 5


In this work we present datasets for both trace-based simulation and real-time testbed evaluation of Dynamic Adaptive Streaming over HTTP (DASH). Our trace-based simulation dataset provides a means of evaluation in frameworks such as NS-2 and NS-3, while our testbed evaluation dataset offers a means of analysing the delivery of content over a physical network and associated adaptation mechanisms at the client. Our datasets are available in both H.264 and H.265 with encoding rates comparative to the representations and resolutions of content distribution providers such as Netflix, Hulu and YouTube. The goal of our dataset is to provide researchers with a sufficiently large dataset, in both number, and duration, of clips which provides a comparison between both encoding schemes. We provide options for evaluating not only different content and genres, but also the underlying encoding metrics, such as transmission cost, segment distribution (the range of the oscillation of the segment sizes) and associated delivery issues such as jitter and re-buffering. Finally, we also offer our datasets in a header-only compressed format, which allows researchers to download the entire dataset and uncompress locally, thus ensuring that our datasets are accessible both online via remote and local servers.


The files are available for download via HTTP. Link:

References and Citation

Use of the datasets in published work should be acknowledged by a full citation to the authors’ papers [RFW16] at the MMSys conference: Proceedings of ACM MMSys’16, Klagenfurt am Wörthersee, Austria, May 10-13, 2016.


  • QZS16: Quinlan, J.J., Zahran, A.H., Sreenan, C.J. Datasets for AVC (H.264) and HEVC (H.265) evaluation of dynamic adaptive streaming over HTTP (DASH), Proceedings of the 7th International Conference on Multimedia Systems, MMSys 2016, art. no. 2938641, pp. 386-391.