research-article

How will Deep Learning Change Internet Video Delivery?

Authors:

Hyunho Yeo,

Sunghyun Do,

Dongsu HanAuthors Info & Claims

HotNets '17: Proceedings of the 16th ACM Workshop on Hot Topics in Networks

Pages 57 - 64

https://doi.org/10.1145/3152434.3152440

Published: 30 November 2017 Publication History

Get Access

Supplementary Material

MP4 File (yeo.mp4)

Download
939.32 MB

References

[1]

2016. ARRIS Gives Us a Hint of the Bandwidth Requirements for VR. http://www.onlinereporter.com/2016/06/17/arris-gives-us-hint-bandwidth-requirements-vr/. (June 2016).

Google Scholar

[2]

V. K. Adhikari, Y. Guo, F. Hao, V. Hilt, Z. L. Zhang, M. Varvello, and M. Steiner. 2015. Measurement Study of Netflix, Hulu, and a Tale of Three CDNs. IEEE/ACM Transactions on Networking 23, 6 (Dec 2015), 1984--1997.

Digital Library

Google Scholar

[3]

Saamer Akhshabi, Ali C Begen, and Constantine Dovrolis. 2011. An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP. In Proc. ACM conference on Multimedia systems. ACM, 157--168.

Digital Library

Google Scholar

[4]

Sean Bell and Kavita Bala. 2015. Learning visual similarity for product design with convolutional neural networks. ACM Transactions on Graphics 34, 4 (2015), 98.

Digital Library

Google Scholar

[5]

Yoshua Bengio et al. 2009. Learning deep architectures for AI. Foundations and trends in Machine Learning 2, 1 (2009), 1--127.

Digital Library

Google Scholar

[6]

Olympic Channel. 2012. Basketball - USA vs Spain - Men's Gold Final | London 2012 Olympic Games. https://www.youtube.com/watch?v=19wUr-CK1Y4&t=8394s/. (Aug. 2012).

Google Scholar

[7]

Olympic Channel. 2012. Usain Bolt Wins 200m Final | London 2012 Olympic Games. https://www.youtube.com/watch?v=LWZQAVtkMBo. (Aug. 2012).

Google Scholar

[8]

Olympic Channel. 2012. Usain Bolt Wins Olympic 100m Gold. https://www.youtube.com/watch?v=2O7K-8G2nwU. (Aug. 2012).

Google Scholar

[9]

Cisco. 2017. Cisco Visual Networking Index: Forecast and Methodology, 2016-2021. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.pdf. (July 2017).

Google Scholar

[10]

Team Coco. 2012. Monologue 02/15/12 - CONAN on TBS. https://www.youtube.com/watch?v=DSzCNC4ypiA&list=PLXi05HpOWLE6eLdYEpYooYoNtO4DZ-w&index=3. (Feb. 2012).

Google Scholar

[11]

Team Coco. 2012. Monologue 03/19/12 - CONAN on TBS. https://www.youtube.com/watch?v=rULPBI9O8d4&index=4&list=PLXi05HpOWLE6eLdYEpYooYoNtO4DZ-w. (Feb. 2012).

Google Scholar

[12]

Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2014. Learning a deep convolutional network for image super-resolution. In European Conference on Computer Vision. Springer, 184--199.

Crossref

Google Scholar

[13]

Chao Dong, Chen Change Loy, and Xiaoou Tang. 2016. Accelerating the super-resolution convolutional neural network. In European Conference on Computer Vision. Springer, 391--407.

Crossref

Google Scholar

[14]

K. Fatahalian. 2016. The Rise of Mobile Visual Computing Systems. IEEE Pervasive Computing 15, 2 (Apr 2016), 8--13.

Digital Library

Google Scholar

[15]

F. H. P. Fitzek and M. Reisslein. 2001. MPEG-4 and H.263 video traces for network performance evaluation. IEEE Network 15, 6 (Nov 2001), 40--54.

Digital Library

Google Scholar

[16]

MATTIAS Fridström. 2016. The bandwidth problem: 5 issues the VR industry must resolve. https://venturebeat.com/2017/05/06/the-bandwidth-problem-5-issues-the-vr-industry-must-resolve/. (May 2016).

Google Scholar

[17]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proc. NIPS. 2672--2680.

Digital Library

Google Scholar

[18]

Fred Halsall. 2000. Multimedia Communications (1st ed.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.

Digital Library

Google Scholar

[19]

Te-Yuan Huang, Nikhil Handigol, Brandon Heller, Nick McKeown, and Ramesh Johari. 2012. Confused, Timid, and Unstable: Picking a Video Streaming Rate is Hard. In Proc. IMC. 225--238.

Digital Library

Google Scholar

[20]

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2016. Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2016).

Google Scholar

[21]

Junchen Jiang, Vyas Sekar, and Hui Zhang. 2012. Improving Fairness, Efficiency, and Stability in HTTP-based Adaptive Video Streaming with FESTIVE. In Proc. ACM CoNEXT.

Digital Library

Google Scholar

[22]

Junchen Jiang, Shijie Sun, Vyas Sekar, and Hui Zhang. 2017. Pytheas: Enabling Data-Driven Quality of Experience Optimization Using Group-Based Exploration-Exploitation. In Proc. USENIX NSDI.

Digital Library

Google Scholar

[23]

Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016. Accurate image super-resolution using very deep convolutional networks. In Proc. IEEE CVPR. 1646--1654.

Crossref

Google Scholar

[24]

Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016. Deeply-recursive convolutional network for image super-resolution. In Proc. IEEE CVPR. 1637--1645.

Crossref

Google Scholar

[25]

Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. 2017. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution. arXiv preprint arXiv:1704.03915 (2017).

Google Scholar

[26]

Hongqiang Harry Liu, Ye Wang, Yang Richard Yang, Hao Wang, and Chen Tian. 2012. Optimizing cost and performance for content multi-homing. In Proc. ACM SIGCOMM. 371--382.

Digital Library

Google Scholar

[27]

Xi Liu, Florin Dobrian, Henry Milner, Junchen Jiang, Vyas Sekar, Ion Stoica, and Hui Zhang. 2012. A case for a coordinated internet video control plane. In Proc. ACM SIGCOMM. 359--370.

Digital Library

Google Scholar

[28]

William Lotter, Gabriel Kreiman, and David Cox. 2016. Deep predictive coding networks for video prediction and unsupervised learning. arXiv preprint arXiv:1605.08104 (2016).

Google Scholar

[29]

Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural Adaptive Video Streaming with Pensieve. In Proc. ACM SIGCOMM. ACM, New York, NY, USA, 197--210.

Digital Library

Google Scholar

[30]

Michael Mathieu, Camille Couprie, and Yann LeCun. 2015. Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015).

Google Scholar

[31]

Khalid Moammer. 2015. Nvidia: Pascal Is 10x Faster Than Maxwell, Launching in 2016 On 16nm - Features 3D Memory, NV-Link and Mixed Precision. http://wccftech.com/nvidia-pascal-gpu-gtc-2015/. (March 2015).

Google Scholar

[32]

Tim Moynihan. 2014. WTF Just Happened: My New HDTV Makes Movies Look Unnaturally Smooth. https://www.wired.com/2014/08/wtf-just-happened-soap-opera-effect/. (Aug. 2014).

Google Scholar

[33]

Matthew K. Mukerjee, David Naylor, Junchen Jiang, Dongsu Han, Srinivasan Seshan, and Hui Zhang. 2015. Practical, Real-time Centralized Control for CDN-based Live Video Delivery. In Proc. ACM SIGCOMM. 311--324.

Digital Library

Google Scholar

[34]

Netflix. {n. d.}. Netflix & Binge: New Binge Scale Reveals TV Series We Devour and Those We Savor. https://media.netflix.com/en/press-releases/netflix-binge-new-binge-scale-reveals-tv-series-we-devour-and-those-we-savor-1. ({n. d.}). Last accessed: July 2017.

Google Scholar

[35]

Erik Nygren, Ramesh K. Sitaraman, and Jennifer Sun. 2010. The Akamai Network: A Platform for High-performance Internet Applications. SIGOPS Oper. Syst. Rev. 44, 3 (Aug. 2010), 2--19.

Digital Library

Google Scholar

[36]

Ooyala. {n. d.}. Ooyala GLOBAL VIDEO INDEX Q1 2017. http://go.ooyala.com/rs/447-EQK-225/images/Ooyala-Global-Video-Index-Q1-2017.pdf. ({n. d.}). Last accessed: July 2017.

Google Scholar

[37]

Roger Pantos and William May. 2017. HTTP Live Streaming. Internet-Draft draft-pantos-http-live-streaming-23. IETF Secretariat. http://www.ietf.org/internet-drafts/draft-pantos-http-live-streaming-23.txt

Google Scholar

[38]

Adrian Pennington. 2017. The realities of making VR and AR streaming a reality. https://knect365.com/media-networks/article/98d3564c-2e6b-43f7-8728-dda3038f818a/the-realities-of-making-vr-and-ar-streaming-a-reality. (July 2017).

Google Scholar

[39]

MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra. 2014. Video (language) modeling: a baseline for generative models of natural videos. arXiv preprint arXiv:1412.6604 (2014).

Google Scholar

[40]

Reuters. {n. d.}. How Network TV Figured Out Binge-Watching. http://fortune.com/2016/03/11/netflix-changing-game-network-tv/. ({n. d.}). Last accessed: July 2017.

Google Scholar

[41]

Sandvine. {n. d.}. 2016 Global Internet Phenomena Report: North America and Latin America. https://www.sandvine.com/downloads/general/global-internet-phenomena/2016/global-internet-phenomena-report-latin-america-and-north-america.pdf. ({n. d.}). Last accessed: July 2017.

Google Scholar

[42]

Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. 2016. RealTime Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Crossref

Google Scholar

[43]

Ying Tai, Jian Yang, and Xiaoming Liu. 2017. Image Super-Resolution via Deep Recursive Residual Network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Crossref

Google Scholar

[44]

Lucas Theis, Wenzhe Shi, Andrew Cunningham, and Ferenc Huszár. 2017. Lossy image compression with compressive autoencoders. arXiv preprint arXiv:1703.00395 (2017).

Google Scholar

[45]

Valley Voices. 2017. Why The Internet Pipes Will Burst When Virtual Reality Takes Off. https://www.forbes.com/sites/valleyvoices/2016/02/09/why-the-internet-pipes-will-burst-if-virtual-reality-takes-off/#7049e4583858. (July 2017).

Google Scholar

[46]

Jiang Wang, Yang Song, Thomas Leung, Chuck Rosenberg, Jingbin Wang, James Philbin, Bo Chen, and Ying Wu. 2014. Learning fine-grained image similarity with deep ranking. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1386--1393.

Digital Library

Google Scholar

[47]

John Watkinson. 2004. The MPEG Handbook: MPEG-1, MPEG-2, MPEG-4. Taylor & Francis.

Digital Library

Google Scholar

[48]

Patrick Wendell, Joe Wenjie Jiang, Michael J. Freedman, and Jennifer Rexford. 2010. DONAR: Decentralized Server Selection for Cloud Services. In Proc. ACM SIGCOMM.

Digital Library

Google Scholar

[49]

Sergey Zagoruyko and Nikos Komodakis. 2015. Learning to compare image patches via convolutional neural networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4353--4361.

Crossref

Google Scholar

[50]

Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J Franklin, Scott Shenker, and Ion Stoica. 2012. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In Proc. USENIX NSDI.

Digital Library

Google Scholar

[51]

Zhang, Zhengdong and Sze, Vivienne. 2017. FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos. In CVPR Workshop on New Trends in Image Restoration and Enhancement.

Google Scholar

[52]

T. Zinner, O. Hohlfeld, O. Abboud, and T. Hossfeld. 2010. Impact of frame rate and resolution on objective QoE metrics. In Proc. International Workshop on Quality of Multimedia Experience (QoMEX). 29--34.

Google Scholar

Cited By

View all

Chai BWu DChen JYang MWang ZHu M(2025)REM: Enabling Real-Time Neural-Enhanced Video Streaming on Mobile Devices Using Macroblock-Aware Lookup TableIEEE Transactions on Mobile Computing10.1109/TMC.2024.349644324:3(2085-2097)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3496443
Zhou XZeng JGe SLiu XQiu T(2025)Collaborative Video Streaming With Super-Resolution in Multi-User MEC NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2024.346168524:2(571-584)Online publication date: Feb-2025
https://doi.org/10.1109/TMC.2024.3461685
Mi LYuan TWang WDai HSun LZheng JChen GFu X(2024)Accelerated Neural Enhancement for Video Analytics With Video Quality AdaptationIEEE/ACM Transactions on Networking10.1109/TNET.2024.337510832:4(3045-3060)Online publication date: Aug-2024
https://doi.org/10.1109/TNET.2024.3375108
Show More Cited By

Index Terms

How will Deep Learning Change Internet Video Delivery?

Index terms have been assigned to the content through auto-classification.

Recommendations

Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
Deep Reinforcement Learning: From Q-Learning to Deep Q-Learning
Neural Information Processing
Abstract
As the two hottest branches of machine learning, deep learning and reinforcement learning both play a vital role in the field of artificial intelligence. Combining deep learning with reinforcement learning, deep reinforcement learning is a method ...
Internet video delivery in youtube: from traffic measurements to quality of experience
DataTraffic Monitoring and Analysis

This chapter investigates HTTP video streaming over the Internet for the YouTube platform. YouTube is used as concrete example and case study for video delivery over the Internet, since it is not only the most popular online video platform, but also ...

Comments

Information & Contributors

Information

Published In

HotNets '17: Proceedings of the 16th ACM Workshop on Hot Topics in Networks

November 2017

206 pages

ISBN:9781450355698

DOI:10.1145/3152434

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 November 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Conference

HotNets-XVI

Sponsor:

SIGCOMM

HotNets-XVI: The 16th ACM Workshop on Hot Topics in Networks

November 30 - December 1, 2017

CA, Palo Alto, USA

Acceptance Rates

HotNets '17 Paper Acceptance Rate 28 of 124 submissions, 23%;

Overall Acceptance Rate 110 of 460 submissions, 24%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

34
Total Citations
View Citations
1,034
Total Downloads

Downloads (Last 12 months)31
Downloads (Last 6 weeks)2

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chai BWu DChen JYang MWang ZHu M(2025)REM: Enabling Real-Time Neural-Enhanced Video Streaming on Mobile Devices Using Macroblock-Aware Lookup TableIEEE Transactions on Mobile Computing10.1109/TMC.2024.349644324:3(2085-2097)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3496443
Zhou XZeng JGe SLiu XQiu T(2025)Collaborative Video Streaming With Super-Resolution in Multi-User MEC NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2024.346168524:2(571-584)Online publication date: Feb-2025
https://doi.org/10.1109/TMC.2024.3461685
Mi LYuan TWang WDai HSun LZheng JChen GFu X(2024)Accelerated Neural Enhancement for Video Analytics With Video Quality AdaptationIEEE/ACM Transactions on Networking10.1109/TNET.2024.337510832:4(3045-3060)Online publication date: Aug-2024
https://doi.org/10.1109/TNET.2024.3375108
Wang SYang JBi S(2024)Adaptive Video Streaming in Multi-Tier Computing Networks: Joint Edge Transcoding and Client EnhancementIEEE Transactions on Mobile Computing10.1109/TMC.2023.326304623:4(2657-2670)Online publication date: Apr-2024
https://doi.org/10.1109/TMC.2023.3263046
Choi MYun WSon SPark SKim J(2024)Joint Delay-Sensitive and Power-Efficient Quality Control of Dynamic Video Streaming Using Adaptive Super-ResolutionIEEE Transactions on Green Communications and Networking10.1109/TGCN.2023.33194378:1(103-117)Online publication date: Mar-2024
https://doi.org/10.1109/TGCN.2023.3319437
Sun LWang WYuan TMi LDai HLiu YFu X(2024)BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on EdgeIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621392(1181-1190)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621392
Jiang STan H(2024)Towards Efficient Neural Video Delivery via Scalable and Dynamic Networks2024 10th International Conference on Big Data Computing and Communications (BigCom)10.1109/BIGCOM65357.2024.00039(225-232)Online publication date: 9-Aug-2024
https://doi.org/10.1109/BIGCOM65357.2024.00039
Yang LLou W(2024)On designing a profitable system model to harmonize the tripartite dissension in content delivery applicationsJournal of Network and Computer Applications10.1016/j.jnca.2024.103965230(103965)Online publication date: Oct-2024
https://doi.org/10.1016/j.jnca.2024.103965
Liborio Filho JOliveira JMelo C(2024)Super-resolution with perceptual quality for improved live streaming delivery on edge computingComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110463248:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110463
Tian WJiang QChen LLi HYan J(2024)Enhanced Asymmetric Invertible Network for Neural Video DeliveryComputer Vision – ACCV 202410.1007/978-981-96-0960-4_23(379-394)Online publication date: 8-Dec-2024
https://doi.org/10.1007/978-981-96-0960-4_23
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Supplementary Material

References

Cited By

Index Terms

Recommendations

Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

Deep Reinforcement Learning: From Q-Learning to Deep Q-Learning

Internet video delivery in youtube: from traffic measurements to quality of experience

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations