Performance of Low-Latency DASH and HLS Streaming in Mobile Networks

Metadata

Publisher: SMPTE — White Plains, NY, USA
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Mot. Imag. J
Volume: 131, No. 7, pp. 26–34
Abstract: Reducing end-to-end streaming latency is critical to Hypertext Transfer Protocol (HTTP)-based live video streaming. There are currently two technologies in this domain: 1) Low-Latency HTTP Live Streaming (LL-HLS) and 2) Low-Latency Dynamic Adaptive Streaming over HTTP (LL-DASH). The latter is sometimes also referred to as Low-Latency Common Media Application Format (LL-CMAF), but effectively it is the same architecture. Several existing implementations of streaming players, as well as encoding and packaging tools, support both technologies. Well-known examples include Apple’s AVplayer, Shaka player, HLS.js, DASH.js, FFmpeg, and so on. In this article, we conduct a performance analysis of such streaming systems. We perform a series of live streaming experiments, repeated using identical video content, encoders, encoding profiles, and network conditions, emulated by using traces of 4G LTE mobile networks from two major operators. We capture several performance metrics, such as average stream bitrate, the amounts of downloaded media data, streaming latency, buffering, frequency of stream switching, and so on. Subsequently, we analyze the captured data and describe the observed differences in the performance of LL-HLS and LL-DASH-based systems.
Publication Date: 2022-08-01
DOI: 10.5594/JMI.2022.3180777
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2022.3180777
Author(s): Bo Zhang
bio
Bo Zhang is a video systems engineer at Brightcove, Inc., Boston, MA. He researches video delivery and playback technologies and builds high-quality, smooth, scalable, and low-latency video streaming software. He has published several research articles in the domains of video streaming and wireless communications. He received a PhD degree in computer science from George Mason University, Fairfax, VA, an MS degree in computer science from the University of Cincinnati, Cincinnati, OH, and a BS degree in computer science from the Huazhong University of Science and Technology, Wuhan, China. He was a recipient of the Best Paper Award from the Association for Computing Machinery (ACM) MSWiM 2011.
Thiago Teixeira
bio
Thiago Teixeira is a software engineer at Brightcove, Inc., Boston, MA. He works across teams to improve the company’s internal design systems and contributes to innovations and new technology research initiatives. He received a PhD degree in computer engineering from the University of Massachusetts at Amherst, Amherst, MA, in 2019, and a BE degree in electrical engineering from Unisinos University, São Leopoldo, Brazil, in 2013. His research interests include computer networks, new internet architectures, and wireless communications.
Yuriy Reznik
bio
Yuriy Reznik is a technology fellow and vice president of research at Brightcove Inc., Boston, MA. Previously, he held engineering and management positions with InterDigital, San Diego, CA, from 2011 to 2016, Qualcomm, San Diego, CA, from 2005 to 2011, and RealNetworks, Seattle, WA, from 1998 to 2005. In 2008, he was a visiting scholar at Stanford University, Stanford, CA. Since 2001, he has been involved in the work of ITU-T SG16 and MPEG standards committees and made contributions to several multimedia coding and delivery standards, including ITU-T H.264/MPEG-4 AVC, MPEG-4 ALS, ITU-T G.718, ITU-T H.265/MPEG-HEVC, and MPEG-DASH. Several technologies, standards, and products that he has helped to develop (RealAudio/RealVideo, ITU-T H.264/MPEG-4 AVC, Zencoder, Brightcove CAE, and MPEG-DASH) have been recognized by the National Academy of Television Arts and Sciences (NATAS) Technology and Engineering Emmy Awards. He received a PhD degree in computer science from Kyiv University, Kyiv, Ukraine. He is a Senior Member of IEEE and the International Society for Optics and Photonics (SPIE), and a member of the Association for Computer Machinary (ACM), Audio Engineering Society (AES), and SMPTE. He is a coauthor of over 150 conference papers and journal articles, and a coinventor of over 70 granted U.S. patents.
Keyword(s): DASH, HAS, HLS, Low-latency Streaming, Performance Evaluation, Video Players
Copyright: © 2022 SMPTE

Bibliographic Reference(s)

Optimizing Mass-Scale Multi-Screen Video Delivery [Active]
ISO.23009-1.2012 — ISO/IEC 23009-1:2012, Information Technology-Dynamic Adaptive Streaming Over HTTP (DASH)-Part 1: Media Presentation Description and Segment Formats. EXTERNAL
T. Karagkioules et al., “Online Learning for Low-Latency Adaptive Streaming,” ACM Multimedia Syst. Conf., Online, June 8–11, 2020. EXTERNAL
D. Raca et al. “Beyond Throughput: A 4G LTE Dataset With Channel and Context Metrics,” ACM Multimedia Syst. Conf., New York, NY, pp. 460–465, 2018. EXTERNAL
Blender Foundation, “Big Buck Bunny Video.” Accessed: May 31, 2022. [Online]. Available: http://bbb3d.renderfarming.net/download.html EXTERNAL
A. Zabrovskiy et al., “AdViSE: Adaptive Video Streaming Evaluation Framework for the Automated Testing of Media Players,” ACM Multimedia Syst. Conf., Taipei, Taiwan, June 20–23, 2017. EXTERNAL
B. Girod, et al., “Advances in Channel-Adaptive Video Streaming,” Wireless Comm. Mobile Comp., 2(6):573–584, 2002. EXTERNAL
Hls.js Player, Accessed: May 31, 2022. [Online]. Available: https://github.com/video-dev/hls.js/ EXTERNAL
DASH Low Latency Server, Accessed: May 31, 2022. [Online]. Available: https://github.com/maxutility2011/node-gpac-dash EXTERNAL
Brightcove Context Aware Encoding. [Online]. Available: https://www.brightcove.com/en/products/online-video-platform/context-aware-encoding/ EXTERNAL
Apple, “Enabling Low-Latency HLS.” Accessed: May 31, 2022. [Online]. Available: https://developer.apple.com/documentation/http_live_streaming/enabling_low-latency_hls EXTERNAL
A. Mondal et al., “EnDASH—A Mobility Adapted Energy Efficient ABR Video Streaming for Cellular Networks,” IFIP Networking Conf., pp. 127–135, 2020. EXTERNAL
HLS Tools, Accessed: May 31, 2022. [Online]. Available: https://developer.apple.com/documentation/http_live_streaming/about_apple_s_http_live_streaming_tools EXTERNAL
AVFoundation, Accessed: May 31, 2022. [Online]. Available: https://developer.apple.com/av-foundation/ EXTERNAL
G. Ribezzo et al., “A DASH 360° Immersive Video Streaming Control System,” Internet Tech. Lett., 3(5), 2020. EXTERNAL
K. Spiteri, R. Urgaonkar, and R. K. Sitaraman, “BOLA: Near-Optimal Bitrate Adaptation for Online Videos,” Annu. IEEE Int. Conf. Comput. Commun., pp. 1–9, 2016. EXTERNAL
B. Zhang, “Low-Latency DASH Streaming Using Open-Source Tools.” Accessed: May 31, 2022. [Online]. Available: ht t p s: // b oz h a n g -26963.me d iu m .c om / low - l ate nc y - d a s h -streaming-using-open-source-tools-f93142ece69d EXTERNAL
D. Wu et al., “Streaming Video Over the Internet: Approaches and Directions,” IEEE Trans. CSVT, 11(3):282–300, 2001. EXTERNAL
K. Durak et al., “Evaluating the Performance of Apple’s Low-Latency HLS,” IEEE Int. Workshop on Multimedia Signal Process., pp. 1–6, Sep. 21–24, 2020. EXTERNAL
A. Bentaleb et al., “Performance Analysis of ACTE: A Bandwidth Prediction Method for Low-latency Chunked Streaming,” ACM Trans. Multimedia Comp., Comm., Appl., 16(2s):1– 24, 2020. EXTERNAL
Z. Li et al., “Probe and Adapt: Rate-Adaptation for HTTP Video Streaming at Scale,” IEEE J Sel. Areas Commun., 32(4):719– 733, April 2014. EXTERNAL
T. Huang et al., “A Buffer-Based Approach to Rate Adaptation: Evidence From a Large Video Streaming Service,” ACM SIGCOMM, pp. 187–198, 2014. EXTERNAL
A. Bentaleb et al., “Bandwidth Prediction in Low-Latency Chunked Streaming,” ACM Workshop on Network and Operating Syst. Support for Digital Audio and Video, Amherst, MA, pp. 7–13, June 21, 2019. EXTERNAL
G. Conklin et al., “Video Coding for Streaming Media Delivery on the Internet,” IEEE Trans. CSVT, 11(3):269–281, 2001. EXTERNAL
Shaka Player, Accessed: May 31, 2022. [Online]. Available: https://github.com/google/shaka-player EXTERNAL
C. Midoglu, et al., “Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks,” ACM Int. Conf. on Multimedia, Nice, France, pp. 2288–2291, Oct. 21–25, 2019. EXTERNAL
C. Storck and F. Figueiredo, “A Performance Analysis of Adaptive Streaming Algorithms in 5G Vehicular Communications in Urban Scenarios,” IEEE Symp. Comput. Commun., pp. 1–7, 2020. EXTERNAL
Y. Reznik et al., “Optimal Design of Encoding Profiles for ABR Streaming,” Proc. Packet Video Workshop, Amsterdam, The Netherlands, June 12, 2018. Accessed: July 8, 2022. [Online], Available: https://www.w3.org/TR/2011/WD-html5-20110113/video.html EXTERNAL
D. Raca et al., “DASHbed: A Testbed Framework for Large Scale Empirical Evaluation of Real-Time DASH in Wireless Scenarios,” ACM Multimedia Syst. Conf., Amherst, MA, pp. 285– 290, June 18–21, 2019. EXTERNAL
A. Bentaleb et al., “A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP,” IEEE Commun. Surv. Tutor., 21(1):562–585, 2019. EXTERNAL
Nginx Web Server. Accessed: May 31, 2022. [Online]. Available: https://www.nginx.com/ EXTERNAL
T. Huang et al., “Hindsight: Evaluate Video Bitrate Adaptation at Scale,” ACM Multimedia Syst. Conf., Amherst, MA, pp. 86–97, June 18–21, 2019. EXTERNAL
European Telecommunication Standards Institute (ETSI) Technical Specification, “MPEG-DASH Profile for Transport of ISO-BMFF Based DVB Services Over IP Based Networks.” Accessed: July 8, 2022. [Online]. Available: https://www.etsi.org/deliver/etsi_ts/103200_103299/103285/01.03.01_60/ts_103285v010301p.pdf EXTERNAL
I. Ayad et al., “A Practical Evaluation of Rate Adaptation Algorithms in HTTP-based Adaptive Streaming,” Elsevier Comput Netw., 133:90–103, 2018. EXTERNAL
Open Broadcast Software. Accessed: May 31, 2022. [Online]. Available: https://obsproject.com/ EXTERNAL
R. Netravali et al., “Mahimahi: Accurate Record-and-Replay for HTTP,” USENIX Annual Tech. Conf., Santa Clara, CA, July 8–10, 2015. EXTERNAL
X. Yin et al., “A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP,” SIGCOMM Comput. Commun., Rev. no. 45(4) 325–338, 2015. EXTERNAL
C. Zhou et al., “A Control-Theoretic Approach to Rate Adaptation for Dynamic HTTP Streaming,” Visual Comm. Image Processing, San Diego, CA, pp. 1–6, 2012. EXTERNAL
M. Lim et al., “When They Go High, We Go Low: Low-Latency Live Streaming in dash.js with LoL,” ACM Multimedia Syst. Conf., Online, June 8–11, 2020. EXTERNAL
S. Hesse, “Design of Scheduling and Rate-Adaptation Algorithms for Adaptive HTTP Streaming,” SPIE 8856, Appl. of Digital Image Process. XXXVI, 88560M, 2013. EXTERNAL
D. Talon et al., “Comparing DASH Adaptation Algorithms in a Real Network Environment,” Eur. Wireless 2019; 25th Eur. Wireless Conf., 2019. EXTERNAL
A. Bentaleb et al., “Data-Driven Bandwidth Prediction Models and Automated Model Selection for Low Latency,” IEEE Trans. on Multimedia, Aug. 2020. EXTERNAL
J. Jiang, V. Sekar, and H. Zhang, “Improving Fairness, Efficiency, and Stability in HTTP-Based Adaptive Video EXTERNAL
S. Sengupta et al., “HotDASH: Hotspot Aware Adaptive Video Streaming Using Deep Reinforcement Learning,” IEEE Int. Conf. on Network Protocols, pp.165–175, 2018. EXTERNAL
FFmpeg, Accessed: May 31, 2022. [Online]. Available: https://www.ffmpeg.org/ EXTERNAL
I. Ozcelik and C. Ersoy, “Low-Latency Live Streaming Over HTTP in Bandwidth-Limited Networks,” IEEE Commun. Lett., 25(2):450–454, 2021. EXTERNAL
DASH Industry Forum, “Low-Latency Modes for DASH.” Accessed: May 31, 2022. [Online]. Available: https://dashif.org/docs/CR-Low-Latency-Live-r8.pdf EXTERNAL
B. Zhang, “Setting Up Your Own Low-Latency HLS Server to Stream from any Source Inputs.” Accessed: May 31, 2022. [Online]. Available: https://bozhang-26963.medium.com/setting-up-your-low-latency-hls-server-to-stream-from-any-source-inputs-de1e757a6688 EXTERNAL
K. Spiteri, R. Sitaraman, and D. Sparacio, “From Theory to Practice: Improving Bitrate Adaptation in the DASH Reference Player,” ACM Trans. Multimedia Comput. Commun. Appl., 15(2s), Article 67, 2019. EXTERNAL
Dash.js Player, Accessed: May 31, 2022. [Online]. Available: https://github.com/Dash-Industry-Forum/dash.js EXTERNAL
B. Taraghi et al., “CAdViSE: Cloud-based Adaptive Video Streaming Evaluation Framework for the Automated Testing of Media Players,” ACM Multimedia Syst. Conf., Online, June 8–11, 2020. EXTERNAL
HTML5 Video. Accessed: May 31, 2022. [Online]. Available: https://www.w3.org/TR/2011/WD-html5-20110113/ video.html EXTERNAL
B. Zhang, T. Teixeira, and Y. Reznik, “Performance of Low-Latency HTTP-based Streaming Players,” ACM Multimedia Syst. Conf., Istanbul, Turkey, Sept. 28–Oct. 1, 2021. EXTERNAL
IETF RFC 8216 [Active*]

Source Data (JSON)

Full registry record with provenance metadata. Open directly: /api/doc/10.5594-JMI.2022.3180777.json

Reference Tree

Explore all references and references to this document, as a navigable tree.

Open Reference Tree

Reference this Doc

Plain text (ISO 690 compliant)

Preview:

Bo Zhang, Thiago Teixeira, and Yuriy Reznik; Performance of Low-Latency DASH and HLS Streaming in Mobile Networks, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 7, August 2022); SMPTE, 2022. Available at https://doi.org/10.5594/JMI.2022.3180777

Snippet:

Bo Zhang, Thiago Teixeira, and Yuriy Reznik; Performance of Low-Latency DASH and HLS Streaming in Mobile Networks, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 7, August 2022); SMPTE, 2022. Available at https://doi.org/10.5594/JMI.2022.3180777

HTML (ISO 690 compliant)

Preview:

Snippet:

<span class="citation">Bo Zhang, Thiago Teixeira, and Yuriy Reznik; <cite>Performance of Low-Latency DASH and HLS Streaming in Mobile Networks</cite>, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 7, August 2022); SMPTE, 2022. Available at <a href="https://doi.org/10.5594/JMI.2022.3180777" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2022.3180777</a></span>

SMPTE's HTML Pub

Preview:

Bo Zhang, Thiago Teixeira, and Yuriy Reznik; Performance of Low-Latency DASH and HLS Streaming in Mobile Networks, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 7, August 2022); SMPTE, 2022
doi: 10.5594/JMI.2022.3180777
url: https://doi.org/10.5594/JMI.2022.3180777

Snippet:

<li>
Bo Zhang, Thiago Teixeira, and Yuriy Reznik; <cite id="bib-10-5594-jmi-2022-3180777">Performance of Low-Latency DASH and HLS Streaming in Mobile Networks</cite>, SMPTE Motion Imaging Journal ( Volume: 131, Issue: 7, August 2022); SMPTE, 2022
<span class="doi">10.5594/JMI.2022.3180777</span>
</li>