Stefan Pham
Patrick Heeren
Abstract
Adaptive bitrate media streaming clients adjust the quality of media content depending on the current network conditions. The shared resource allocation (SRA) feature defined in MPEG-SAND (server and network assisted DASH) allows servers to allocate bandwidth to streaming clients. This enables coordination and prioritization of clients that are connected to the same network bottleneck (e.g., to maximize the number of clients that can play back a stream fluently). In this article, we evaluate different bandwidth limitation strategies and analyze the effects on the clients. For this purpose, a testbed using multiple Raspberry Pis was created. The results show that in various scenarios, SRA improves the fairness and the QoE of streaming sessions. Solely allocating a maximum quality level to the client is not sufficient in some cases. Therefore, additional means, such as limiting bandwidth on the client or traffic shaping with software-defined networking for SRA, are evaluated.
- Man7.org. 2001. tc—Linux Manual Page. Retrieved October 14, 2018 from http://man7.org/linux/man-pages/man8/tc.8.html.Google Scholar
- Man7.org. 2015. tc-police—Linux Manual Page. Retrieved October 14, 2018 from http://man7.org/linux/man-pages/man8/tc-police.8.html.Google Scholar
- Github. 2019. RYU SDN Framework. Retrieved November 16, 2019 from https://ryu-sdn.org.Google Scholar
- Saamer Akhshabi, Lakshmi Anantakrishnan, Ali C. Begen, and Constantine Dovrolis. 2012. What happens when HTTP adaptive streaming players compete for bandwidth? InProceedings of the 22nd International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV’12). 9--14.Google ScholarDigital Library
- Abdelhak Bentaleb, Ali C. Begen, and Roger Zimmermann. 2016. SDNDASH: Improving QoE of HTTP adaptive streaming using software defined networking. In Proceedings of the 24th ACM International Conference on Multimedia. ACM, New York, NY, 1296--1305.Google ScholarDigital Library
- Abdelhak Bentaleb, Ali C. Begen, Roger Zimmermann, and Saad Harous. 2017. SDNHAS: An SDN-enabled architecture to optimize QoE in HTTP adaptive streaming. IEEE Transactions on Multimedia 19, 10 (2017), 2136--2151.Google ScholarDigital Library
- Cisco Systems. 2020. Cisco Annual Internet Report (2018-2023). Retrieved June 15, 2020.Google Scholar
- Giuseppe Cofano, Luca De Cicco, Thomas Zinner, Anh Nguyen-Ngoc, Phuoc Tran-Gia, and Saverio Mascolo. 2016. Design and experimental evaluation of network-assisted strategies for HTTP adaptive streaming. In Proceedings of the 7th International Conference on Multimedia Systems. ACM, New York, NY, 3.Google ScholarDigital Library
- DASH Industry Forum. 2016. DASH-IF Position Paper: Server and Network Assisted DASH (SAND). Retrieved December 14, 2017 from https://dashif.org/docs/SAND-Whitepaper-Dec13-final.pdf.Google Scholar
- Panagiotis Georgopoulos, Yehia Elkhatib, Matthew Broadbent, Mu Mu, and Nicholas Race. 2013. Towards network-wide QoE fairness using OpenFlow-assisted adaptive video streaming. In Proceedings of the 2013 ACM SIGCOMM Workshop on Future Human-Centric Multimedia Networking. ACM, New York, NY, 15--20.Google ScholarDigital Library
- Tobias Hoßfeld, Lea Skorin-Kapov, Poul E. Heegaard, and Martín Varela. 2018. A new QoE fairness index for QoE management. Quality and User Experience 3, 1 (2018), 4. DOI:https://doi.org/10.1007/s41233-018-0017-xGoogle ScholarCross Ref
- ISO/IEC 23009-5:2017 2017. Information Technology—Dynamic Adaptive Streaming over HTTP (DASH)—Part 5: Server and Network Assisted DASH (SAND). Standard. International Organization for Standardization, Geneva, Switzerland.Google Scholar
- Jan Willem Kleinrouweler, Sergio Cabrero, and Pablo Cesar. 2016. Delivering stable high-quality video: An SDN architecture with DASH assisting network elements. In Proceedings of the 7th International Conference on Multimedia Systems. ACM, New York, NY, 4.Google ScholarDigital Library
- Jan Willem Kleinrouweler, Britta Meixner, and Pablo Cesar. 2017. Improving video quality in crowded networks using a DANE. In Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, New York, NY, 73--78.Google ScholarDigital Library
- Nick McKeown, Tom Anderson, Hari Balakrishnan, Guru Parulkar, Larry Peterson, Jennifer Rexford, Scott Shenker, and Jonathan Turner. 2008. OpenFlow: Enabling innovation in campus networks. ACM SIGCOMM Computer Communication Review 38, 2 (2008), 69--74.Google ScholarDigital Library
- RajaRevanth Narisetty, Levent Dane, Anatoliy Malishevskiy, Deniz Gurkan, Stuart Bailey, Sandhya Narayan, and Shivaram Mysore. 2013. OpenFlow configuration protocol: Implementation for the of management plane. In Proceedings of the 2013 2nd GENI Research and Educational Experiment Workshop. IEEE, Los Alamitos, CA, 66--67.Google ScholarDigital Library
- David Palma, Joao Goncalves, Bruno Sousa, Luis Cordeiro, Paulo Simoes, Sachin Sharma, and Dimitri Staessens. 2014. The QueuePusher: Enabling queue management in openflow. In Proceedings of the European Workshop on Software Defined Networking (EWSDN’14). IEEE, Los Alamitos, CA, 125--126.Google ScholarDigital Library
- Ben Pfaff, Justin Pettit, Teemu Koponen, Ethan Jackson, Andy Zhou, Jarno Rajahalme, Jesse Gross, et al. 2015. The design and implementation of Open vSwitch. In Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI’15). 117--130.Google ScholarDigital Library
- Stefan Pham, Patrick Heeren, Daniel Silhavy, and Stefan Arbanowski. 2019. Evaluation of shared resource allocation using SAND for ABR streaming. In Proceedings of the 10th ACM Multimedia Systems Conference (MMSys’19). ACM, New York, NY, 165--174. DOI:https://doi.org/10.1145/3304109.3306227Google ScholarDigital Library
- M. Said Seddiki, Muhammad Shahbaz, Sean Donovan, Sarthak Grover, Miseon Park, Nick Feamster, and Ye-Qiong Song. 2014. FlowQoS: QoS for the rest of us. In Proceedings of the 3rd Workshop on Hot Topics in Software Defined Networking. ACM, New York, NY, 207--208.Google ScholarDigital Library
- Kevin Spiteri, Rahul Urgaonkar, and Ramesh K. Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications (INFOCOM’16). IEEE, Los Alamitos, CA, 1--9.Google Scholar
- Thomas Stockhammer. 2011. Dynamic adaptive streaming over HTTP: Standards and design principles. In Proceedings of the 2nd Annual ACM Conference on Multimedia Systems. ACM, New York, NY, 133--144.Google ScholarDigital Library
- Emmanuel Thomas, Oskar van Deventer, Thomas Stockhammer, Ali Begen, Mary-Luc Champel, and Ozgur Oyman. 2016. Applications and deployments of server and network assisted DASH (SAND). In Proceedings of the IBC 2016 Conference (IBC’16). DOI:https://doi.org/10.1049/ibc.2016.0022Google ScholarCross Ref
- Anatoliy Zabrovskiy, Evgeny Petrov, Evgeny Kuzmin, and Christian Timmerer. 2017. Automated performance evaluation of adaptive HTML5 player deployments. In Proceedings of the 21st Conference of Open Innovations Association FRUCT.Google Scholar
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