research-article

NeuroScaler: neural video enhancement at scale

Authors:

Dongsu HanAuthors Info & Claims

SIGCOMM '22: Proceedings of the ACM SIGCOMM 2022 Conference

Pages 795 - 811

https://doi.org/10.1145/3544216.3544218

Published: 22 August 2022 Publication History

Abstract

High-definition live streaming has experienced tremendous growth. However, the video quality of live video is often limited by the streamer's uplink bandwidth. Recently, neural-enhanced live streaming has shown great promise in enhancing the video quality by running neural super-resolution at the ingest server. Despite its benefit, it is too expensive to be deployed at scale. To overcome the limitation, we present NeuroScaler, a framework that delivers efficient and scalable neural enhancement for live streams. First, to accelerate end-to-end neural enhancement, we propose novel algorithms that significantly reduce the overhead of video super-resolution, encoding, and GPU context switching. Second, to maximize the overall quality gain, we devise a resource scheduler that considers the unique characteristics of the neural-enhancing workload. Our evaluation on a public cloud shows NeuroScaler reduces the overall cost by 22.3× and 3.0--11.1× compared to the latest per-frame and selective neural-enhancing systems, respectively.

Supplementary Material

PDF File (p795-yeo-supp.pdf)

Supplemental material.

Download
76.65 KB

References

[1]

720p Is The Preferred Broadcasting Resolution On Twitch. https://parsec.app/blog/720p-is-the-preferred-broadcasting-resolution-on-twitch-e5385a3ef08f.

[2]

Amazon EC2 Inf1 Instances Website. https://cloud.google.com/tpu.

[3]

Apache TVM Official Website. https://tvm.apache.org/.

[4]

Apple HTTP Live Streaming Official Website. https://developer.apple.com/streaming/.

[5]

AV1 Official Website. https://aomedia.org/av1/.

[6]

AWS EC2 C6i Instance Official Website. https://aws.amazon.com/ko/ec2/instance-types/c6i/.

[7]

AWS EC2 DL1 Instance Official Website. https://aws.amazon.com/ec2/instance-types/.

[8]

AWS EC2 G4 Instance Official Website. https://aws.amazon.com/ec2/instance-types/g4/.

[9]

AWS EC2 P4Instance Official Website. https://aws.amazon.com/ec2/instance-types/p4/.

[10]

AWS F1 Instance Website. https://aws.amazon.com/ec2/instance-types/f1/.

[11]

The Best Streaming Bitrate and Resolutions for Twitch. https://bit.ly/32rX2Pe.

[12]

Cisco Visual Networking Index Report. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/complete-white-paper-c11-481360.pdf.

[13]

Cloud TPU Website. https://aws.amazon.com/ec2/instance-types/inf1/.

[14]

CPU cores and threads per CPU core per instance type. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/cpu-options-supported-instances-values.html.

[15]

DASH Industry Forum Official Website. https://dashif.org/.

[16]

Dataset: Chat Youtube video. https://www.youtube.com/watch?v=TJoAy944MoM.

[17]

Dataset: Fortnite Youtube video. https://www.youtube.com/watch?v=LW4asVQsew0.

[18]

Dataset: GTA5 Youtube video. https://www.youtube.com/watch?v=jYglXGpaBSg.

[19]

Dataset: League of legend Youtube video. https://www.youtube.com/watch?v=Ku_q0O_kgGE.

[20]

Dataset: Minecraft Youtube video. https://www.youtube.com/watch?v=5gf3AGv7o8.

[21]

Dataset: Valorant Youtube video. https://www.youtube.com/watch?v=dqhVl11DypA.

[22]

Deploying NVIDIA Triton at Scale with MIG and Kubernetes. https://bit.ly/31X8zVQ.

[23]

FFmpeg Official Website. https://www.ffmpeg.org/.

[24]

gRPC Github Repository. https://github.com/grpc/grpc.

[25]

H.264 Official Website. https://www.itu.int/rec/T-REC-H.264-200305-S/en.

[26]

H.265 Official Website. https://www.itu.int/rec/T-REC-H.265.

[27]

How Long is Twitch Stream Delay. https://onetwostream.com/blog/twitch-delay/.

[28]

How to ensure a full live-streaming experience. https://www.techradar.com/news/how-to-ensure-a-full-live-streaming-experience.

[29]

Intel® Core™ i9-9900K Processor Specifications. https://ark.intel.com/content/www/us/en/ark/products/186605/intel-core-i99900k-processor-16m-cache-up-to-5-00-ghz.html.

[30]

Kakadu Official Website. https://kakadusoftware.com/.

[31]

Kubernetes Load Balancer Document. https://kubernetes.io/docs/concepts/services-networking/_print/.

[32]

libjpeg-turbo Github Repository. https://github.com/libjpeg-turbo/libjpeg-turbo.

[33]

libvpx Github Repository. https://github.com/webmproject/libvpx.

[34]

Most watched games on Twitch in 2020. https://sullygnome.com/games/2020/watched.

[35]

NGCodec uses Amazon EC2 F1 instances with custom FPGAs running 4k Video Compression. www.prlog.org/12604725.

[36]

NGINX Load Balancer Document. https://docs.nginx.com/nginx/admin-guide/load-balancer/http-load-balancer/.

[37]

NVIDIA A100 Official Website. https://www.nvidia.com/en-us/data-center/a100/.

[38]

NVIDIA Maxine Hits the Scene to Create Real-Time Video Experiences. https://blogs.nvidia.com/blog/2021/04/12/ai-real-time-video-maxine/.

[39]

NVIDIA Maxine Official Website. https://developer.nvidia.com/maxine.

[40]

NVIDIA NVENC Documentation. https://docs.nvidia.com/video-technologies/video-codec-sdk/ffmpeg-with-nvidia-gpu/.

[41]

NVIDIA T4 Official Website. https://www.nvidia.com/en-gb/data-center/tesla-t4/.

[42]

NVIDIA TensorRT Official Website. https://developer.nvidia.com/tensorrt.

[43]

NVIDIA V100 Official Website. https://www.nvidia.com/en-gb/data-center/tesla-v100/.

[44]

PNG Standard Website. https://www.iso.org/standard/29581.html.

[45]

PyTorch Official Website. https://pytorch.org/.

[46]

Real Time Streaming Protocol (RTSP) Specification. https://tools.ietf.org/html/rfc2326.

[47]

Real-time Transport Protocol (RTP) Specification. https://tools.ietf.org/html/rfc3550.

[48]

Scalable Video Coding (SVC) Extension for WebRTC. https://www.w3.org/TR/webrtc-svc/.

[49]

Snapdragon 855+/860 Mobile Platform. https://www.qualcomm.com/products/snapdragon-855-plus-and-860-mobile-platform.

[50]

SoftBank Solves Key Mobile Edge Computing Challenges Using NVIDIA Maxine. https://bit.ly/3dJNq47.

[51]

Streaming Ad Revenue to Double by 2026. https://yhoo.it/329mAjP.

[52]

Sub-Second Latency Streaming & Live Viewer Interactivity Changing the Video Landscape. https://www.limelight.com/resources/tech-brief/sub-second-latency-streaming-changing-the-video-landscape/.

[53]

TensorRT Official Website. https://developer.nvidia.com/tensorrt.

[54]

Touchcast Unveils World's First AI-Powered Event Platform Boosted by NVIDIA Maxine Technology. https://touchcast.com/newsroom/nvidia-partnership?utm_campaign=NVIDIA%20Partnership&utm_source=NVIDIA.

[55]

Toward A Practical Perceptual Video Quality Metric. https://netflixtechblog.com/toward-a-practical-perceptual-video-quality-metric-653f208b9652.

[56]

Twitch Broadcasting Guidelines. https://stream.twitch.tv/encoding/.

[57]

Twitch Official Website. https://www.twitch.tv/.

[58]

Twitch Revenue and Usage Statistics (2022). https://www.businessofapps.com/data/twitch-statistics/.

[59]

Twitch Statistics. https://backlinko.com/twitch-users.

[60]

Twitch Stream Delay: Everything You Should Know. https://bit.ly/3HGbPpI.

[61]

VP9 codec parameters. https://www.webmproject.org/docs/encoder-parameters/.

[62]

WebM Official Website. https://www.webmproject.org/.

[63]

WebRTC 1.0: Real-Time Communication Between Browsers. https://www.w3.org/TR/webrtc/.

[64]

WebRTC Official Website. https://webrtc.org/.

[65]

WHAT AUDIENCES EXPECT FROM LIVE VIDEO. https://lp.livestream.com/rs/582-GOU-684/images/NYmag.pdf.

[66]

Wowza Transcoding Documents. https://www.wowza.com/docs/wowza-transcoder.

[67]

Xiaomi Mi9 Specifications. https://www.gsmarena.com/xiaomi_mi_9-9507.php.

[68]

YouTube dataset (Product review). https://www.youtube.com/watch?v=qY2BdjhPmSE.

[69]

Youtube Live Encoding Guidelines. https://support.google.com/youtube/answer/2853702?hl=en.

[70]

Youtube Website. https://www.twitch.tv/.

[71]

Duin Baek, Mallesham Dasari, Samir R Das, and Jihoon Ryoo. 2021. dcSR: Practical Video Quality Enhancement Using Data-Centric Super Resolution. (2021).

[72]

Duin Baek, Mallesham Dasari, Samir R. Das, and Jihoon Ryoo. 2021. DcSR: Practical Video Quality Enhancement Using Data-Centric Super Resolution. In Proceedings of the 17th International Conference on Emerging Networking EXperiments and Technologies (CoNEXT '21). Association for Computing Machinery, New York, NY, USA, 336--343.

Digital Library

[73]

Gisle Bjontegaard. 2001. Calculation of average PSNR differences between RD-curves. VCEG-M33 (2001).

[74]

Ying Chen, Qing Li, Aoyang Zhang, Longhao Zou, Yong Jiang, Zhimin Xu, Junlin Li, and Zhenhui Yuan. 2021. Higher quality live streaming under lower uplink bandwidth: an approach of super-resolution based video coding. In Proceedings of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. 74--81.

Digital Library

[75]

Daniel Crankshaw, Xin Wang, Guilio Zhou, Michael J Franklin, Joseph E Gonzalez, and Ion Stoica. 2017. Clipper: A {Low-Latency} Online Prediction Serving System. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). 613--627.

[76]

M. Dasari, A. Bhattacharya, S. Vargas, P. Sahu, A. Balasubramanian, and S. R. Das. 2020. Streaming 360° Videos using Super-resolution. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM).

[77]

Florin Dobrian, Vyas Sekar, Asad Awan, Ion Stoica, Dilip Joseph, Aditya Ganjam, Jibin Zhan, and Hui Zhang. 2011. Understanding the impact of video quality on user engagement. ACM SIGCOMM computer communication review 41, 4 (2011), 362--373.

[78]

Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2015. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence 38, 2 (2015), 295--307.

[79]

Sadjad Fouladi, John Emmons, Emre Orbay, Catherine Wu, Riad S Wahby, and Keith Winstein. 2018. Salsify: Low-latency network video through tighter integration between a video codec and a transport protocol. In 15th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 18). 267--282.

[80]

David Freedman, Robert Pisani, and Roger Purves. 2007. Statistics (international student edition). Pisani, R. Purves, 4th edn. WW Norton & Company, New York (2007).

[81]

Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, and Jonathan Mace. 2020. Serving {DNNs} like Clockwork: Performance Predictability from the Bottom Up. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). 443--462.

[82]

Qi Huang, Petchean Ang, Peter Knowles, Tomasz Nykiel, Iaroslav Tverdokhlib, Amit Yajurvedi, Paul Dapolito IV, Xifan Yan, Maxim Bykov, Chuen Liang, et al. 2017. SVE: Distributed video processing at Facebook scale. In Proceedings of the 26th Symposium on Operating Systems Principles. 87--103.

Digital Library

[83]

Junchen Jiang, Shijie Sun, Vyas Sekar, and Hui Zhang. 2017. Pytheas: Enabling data-driven quality of experience optimization using group-based exploration-exploitation. In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 17). 393--406.

[84]

Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for realtime style transfer and super-resolution. In European conference on computer vision. Springer, 694--711.

[85]

Jaehong Kim, Youngmok Jung, Hyunho Yeo, Juncheol Ye, and Dongsu Han. 2020. Neural-enhanced live streaming: Improving live video ingest via online learning. In Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication. 107--125.

Digital Library

[86]

Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1646--1654.

[87]

X. Kong, H. Zhao, Y. Qiao, and C. Dong. 2021. ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, Los Alamitos, CA, USA, 12011--12020.

[88]

Royson Lee, Stylianos I Venieris, Lukasz Dudziak, Sourav Bhattacharya, and Nicholas D Lane. 2019. Mobisr: Efficient on-device super-resolution through heterogeneous mobile processors. In The 25th Annual International Conference on Mobile Computing and Networking. 1--16.

Digital Library

[89]

Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. 2017. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 136--144.

[90]

Hongqiang Harry Liu, Ye Wang, Yang Richard Yang, Hao Wang, and Chen Tian. 2012. Optimizing cost and performance for content multihoming. In Proceedings of the ACM SIGCOMM 2012 conference on Applications, technologies, architectures, and protocols for computer communication. 371--382.

Digital Library

[91]

Zhenxiao Luo, Zelong Wang, Jinyu Chen, Miao Hu, Yipeng Zhou, Tom ZJ Fu, and Di Wu. 2021. CrowdSR: enabling high-quality video ingest in crowdsourced livecast via super-resolution. In Proceedings of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. 90--97.

Digital Library

[92]

Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural adaptive video streaming with pensieve. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication. 197--210.

Digital Library

[93]

Devdeep Ray, Jack Kosaian, KV Rashmi, and Srinivasan Seshan. 2019. Vantage: optimizing video upload for time-shifted viewing of social live streams. In Proceedings of the ACM Special Interest Group on Data Communication. 380--393.

Digital Library

[94]

Francisco Romero, Qian Li, Neeraja J Yadwadkar, and Christos Kozyrakis. 2021. {INFaaS}: Automated Model-less Inference Serving. In 2021 USENIX Annual Technical Conference (USENIX ATC 21). 397--411.

[95]

Haichen Shen, Lequn Chen, Yuchen Jin, Liangyu Zhao, Bingyu Kong, Matthai Philipose, Arvind Krishnamurthy, and Ravi Sundaram. 2019. Nexus: A GPU cluster engine for accelerating DNN-based video analysis. In Proceedings of the 27th ACM Symposium on Operating Systems Principles. 322--337.

Digital Library

[96]

Kevin Spiteri, Rahul Urgaonkar, and Ramesh K Sitaraman. 2020. BOLA: Near-optimal bitrate adaptation for online videos. IEEE/ACM Transactions on Networking 28, 4 (2020), 1698--1711.

Digital Library

[97]

Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu. 2018. A survey on deep transfer learning. In International conference on artificial neural networks. Springer, 270--279.

[98]

Yiding Wang, Weiyan Wang, Duowen Liu, Xin Jin, Junchen Jiang, and Kai Chen. 2021. Enabling Edge-Cloud Video Analytics for Robotics Applications. In Proceedings of the IEEE International Conference on Computer Communications, Virtual Conference. 10--13.

Digital Library

[99]

Yiding Wang, Weiyan Wang, Duowen Liu, Xin Jin, Junchen Jiang, and Kai Chen. 2021. Enabling Edge-Cloud Video Analytics for Robotics Applications. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications. 1--10.

Digital Library

[100]

Yiding Wang, Weiyan Wang, Junxue Zhang, Junchen Jiang, and Kai Chen. 2019. Bridging the Edge-Cloud Barrier for Real-Time Advanced Vision Analytics. In Proceedings of the 11th USENIX Conference on Hot Topics in Cloud Computing (HotCloud'19). USENIX Association, USA, 18.

Digital Library

[101]

Hyunho Yeo, Chan Ju Chong, Youngmok Jung, Juncheol Ye, and Dongsu Han. 2020. Nemo: enabling neural-enhanced video streaming on commodity mobile devices. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 1--14.

Digital Library

[102]

Hyunho Yeo, Youngmok Jung, Jaehong Kim, Jinwoo Shin, and Dongsu Han. 2018. Neural adaptive content-aware internet video delivery. In 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18). 645--661.

[103]

Xiaoqi Yin, Abhishek Jindal, Vyas Sekar, and Bruno Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. In Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. 325--338.

Digital Library

[104]

Anlan Zhang, Chendong Wang, Bo Han, and Feng Qian. 2017. YuZu: Super-resolution Enhanced Volumetric Video Streaming. In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 22). 393--406.

[105]

Anlan Zhang, Chendong Wang, Bo Han, and Feng Qian. 2021. Efficient Volumetric Video Streaming Through Super Resolution. In Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications. 106--111.

Digital Library

[106]

Cong Zhang and Jiangchuan Liu. 2015. On Crowdsourced Interactive Live Streaming: A Twitch.Tv-Based Measurement Study. In Proceedings of the 25th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV '15). 55--60.

Digital Library

[107]

Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. 2018. Residual dense network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2472--2481.

[108]

Zhengdong Zhang and Vivienne Sze. 2017. FAST: A framework to accelerate super-resolution processing on compressed videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 19--28.

[109]

Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, et al. 2020. Ansor: Generating high-performance tensor programs for deep learning. In 14th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 20). 863--879.

[110]

Size Zheng, Yun Liang, Shuo Wang, Renze Chen, and Kaiwen Sheng. 2020. Flextensor: An automatic schedule exploration and optimization framework for tensor computation on heterogeneous system. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems. 859--873.

Digital Library

[111]

Anfu Zhou, Huanhuan Zhang, Guangyuan Su, Leilei Wu, Ruoxuan Ma, Zhen Meng, Xinyu Zhang, Xiufeng Xie, Huadong Ma, and Xiaojiang Chen. 2019. Learning to coordinate video codec with transport protocol for mobile video telephony. In The 25th Annual International Conference on Mobile Computing and Networking. 1--16.

Digital Library

[112]

Xiao Zhu, Subhabrata Sen, and Z Morley Mao. 2021. Livelyzer: analyzing the first-Mile ingest performance of live video streaming. In Proceedings of the 12th ACM Multimedia Systems Conference. 36--50.

Digital Library

Cited By

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
Cheng YZhang ZLi HArapin AZhang YZhang QLiu YDu KZhang XYan FMazumdar AFeamster NJiang JVanbever LZhang I(2024)GRACEProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691854(509-531)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691854
Show More Cited By

Index Terms

NeuroScaler: neural video enhancement at scale

Recommendations

Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning
SIGCOMM '20: Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication

Live video accounts for a significant volume of today's Internet video. Despite a large number of efforts to enhance user quality of experience (QoE) both at the ingest and distribution side of live video, the fundamental limitations are that streamer's ...
Higher quality live streaming under lower uplink bandwidth: an approach of super-resolution based video coding
NOSSDAV '21: Proceedings of the 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video

With the growing popularity of live streaming, high video quality and low latency with limited uplink bandwidth have become a significant challenge. In this study, we propose Live Super-Resolution Based Video Coding (LiveSRVC), a novel video uploading ...
Meerkat and Periscope: I Stream, You Stream, Apps Stream for Live Streams
CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

We conducted a mixed methods study of the use of the Meerkat and Periscope apps for live streaming video and audio broadcasts from a mobile device. We crowdsourced a task to describe the content, setting, and other characteristics of 767 live streams. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGCOMM '22: Proceedings of the ACM SIGCOMM 2022 Conference

August 2022

858 pages

ISBN:9781450394208

DOI:10.1145/3544216

General Chairs:
Fernando Kuipers
Delft University of Technology
,
Ariel Orda
Technion Israel Institute of Technology

Copyright © 2022 ACM.

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 ACM 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]

Sponsors

SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 August 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Author Tags

Qualifiers

Research-article

Funding Sources

Institute for Information & communications Technology Promotion (IITP)

Conference

SIGCOMM '22

Sponsor:

SIGCOMM

SIGCOMM '22: ACM SIGCOMM 2022 Conference

August 22 - 26, 2022

Amsterdam, Netherlands

Acceptance Rates

Overall Acceptance Rate 462 of 3,389 submissions, 14%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
1,993
Total Downloads

Downloads (Last 12 months)406
Downloads (Last 6 weeks)56

Reflects downloads up to 17 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

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
Cheng YZhang ZLi HArapin AZhang YZhang QLiu YDu KZhang XYan FMazumdar AFeamster NJiang JVanbever LZhang I(2024)GRACEProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691854(509-531)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691854
He ZYang YLi SQiu LDai DYang Y(2024)VIGOR: Reviving Cloud Gaming SessionsProceedings of the ACM on Networking10.1145/36964032:CoNEXT4(1-20)Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1145/3696403
Sun CXu KAntichi GMarina M(2024)NetGSR: Towards Efficient and Reliable Network Monitoring with Generative Super ResolutionProceedings of the ACM on Networking10.1145/36964002:CoNEXT4(1-27)Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1145/3696400
Guo BBao JChai BWu DHu MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Lumos: Optimizing Live 360-degree Video Upstreaming via Spatial-Temporal Integrated Neural EnhancementProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681305(7210-7219)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681305
Wang LSingh SChakareski JHajiesmaili MSitaraman R(2024)BONES: Near-Optimal Neural-Enhanced Video StreamingProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36560148:2(1-28)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3656014
Ao APark S(2024) Applying Transformer-Based Computer Vision Models to Adaptive Bitrate Allocation for 360 ○ Live Streaming 2024 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC57260.2024.10571028(1-6)Online publication date: 21-Apr-2024
https://doi.org/10.1109/WCNC57260.2024.10571028
Jing WLiu CCai HWen XLu ZWang ZZhang H(2024)MEC-Based Super-Resolution Enhanced Adaptive Video Streaming Optimization for Mobile Networks With Satellite BackhaulIEEE Transactions on Network and Service Management10.1109/TNSM.2024.337769321:3(2977-2991)Online publication date: Jun-2024
https://doi.org/10.1109/TNSM.2024.3377693
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

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.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten