dashc: a highly scalable client emulator for DASH video

In this paper we introduce a client emulator for experimenting with DASH video. dashc is a standalone, compact, easy-to-build and easy-to-use command line software tool. The design and implementation of dashc were motivated by the pressing need to conduct network experiments with large numbers of video clients. The highly scalable dashc has low CPU and memory usage. dashc collects necessary statistics about video delivery performance in a convenient format, facilitating thorough post hoc analysis. The code of dashc is modular and new video adaptation algorithm can easily be added. We compare dashc to a state-of-the art client and demonstrate its efficacy for large-scale experiments using the Mininet virtual network.

[1]  Saverio Mascolo,et al.  TAPAS: A Tool for rApid Prototyping of Adaptive Streaming algorithms , 2014, VideoNext '14.

[2]  Adam Wolisz,et al.  Simulation Framework for HTTP-Based Adaptive Streaming Applications , 2017, WNS3.

[3]  Te-Yuan Huang,et al.  A buffer-based approach to rate adaptation: evidence from a large video streaming service , 2015, SIGCOMM 2015.

[4]  Deep Medhi,et al.  SARA: Segment aware rate adaptation algorithm for dynamic adaptive streaming over HTTP , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[5]  Pablo César,et al.  Improving Video Quality in Crowded Networks Using a DANE , 2017, NOSSDAV.

[6]  Christian Timmerer,et al.  AdViSE: Adaptive Video Streaming Evaluation Framework for the Automated Testing of Media Players , 2017, MMSys.

[7]  Ali C. Begen,et al.  What happens when HTTP adaptive streaming players compete for bandwidth? , 2012, NOSSDAV '12.

[8]  Christian Esteve Rothenberg,et al.  Mininet-WiFi: Emulating software-defined wireless networks , 2015, 2015 11th International Conference on Network and Service Management (CNSM).

[9]  Ali C. Begen,et al.  Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale , 2013, IEEE Journal on Selected Areas in Communications.

[10]  Sujit Dey,et al.  Deriving and Validating User Experience Model for DASH Video Streaming , 2015, IEEE Transactions on Broadcasting.

[11]  Ahmed H. Zahran,et al.  Datasets for AVC (H.264) and HEVC (H.265) evaluation of dynamic adaptive streaming over HTTP (DASH) , 2016, MMSys.

[12]  Thomas Stockhammer,et al.  Dynamic adaptive streaming over HTTP --: standards and design principles , 2011, MMSys.

[13]  Filip De Turck,et al.  QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming , 2016, ACM Trans. Multim. Comput. Commun. Appl..

[14]  Björn F. Postema,et al.  Capabilities of Raspberry Pi 2 for Big Data and Video Streaming Applications in Data Centres , 2016, MMB/DFT.

[15]  Ahmed H. Zahran,et al.  ARBITER: Adaptive rate-based intelligent HTTP streaming algorithm , 2016, 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).