research-article

Leveraging QoE Heterogenity for Large-Scale Livecaset Scheduling

Authors:

Rui-Xiao Zhang,

Jiangchuan Liu,

Lifeng SunAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 3678 - 3686

https://doi.org/10.1145/3394171.3413918

Published: 12 October 2020 Publication History

Abstract

Livecast streaming has received great success in recent years. Although many prior efforts have suggested that dynamic viewer scheduling according to the quality of service (QoS) can improve user engagement, they may suffer inefficiency due to their ignorance of viewer heterogeneity in how the QoS impact quality of experience (QoE).

In this paper, we conduct measurement studies over large-scale data provided by a top livecast platform in China. We observe that QoE is influenced by a lot of QoS and non-QoS factors, and most importantly, the QoE sensitivity to QoS metrics can vary significantly among viewers. Inspired by the above insights, we propose HeteroCast, a novel livecast scheduling framework for intelligent viewer scheduling based on viewer heterogeneity. In detail, HeteroCast addresses this concern by solving two sub-problems. For the first sub-problem (i.e., the QoE modeling problem), we use the deep factorization machine (DeepFM) based method to precisely map complicated factors (QoS and non-QoS factors) to QoE and build the QoE model. For the second sub-problem (i.e., the QoE-aware scheduling problem), we use a graph-matching method to generate the best viewer allocation policy for each CDN provider. Specifically, by using some pruning techniques, HeteroCast only introduces slight overhead and can well adapt to the large-scale livecast scenario. Through extensive evaluation on real-world traces, HeteroCast is demonstrated to increase the average QoE by 8.87%-10.09%.

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References

[1]

Vijay Kumar Adhikari, Yang Guo, Fang Hao, Matteo Varvello, Volker Hilt, Moritz Steiner, and Zhili Zhang. 2012. Unreeling netflix: Understanding and improving multi-CDN movie delivery. (2012), 1620--1628.

[2]

Athula Balachandran, Vyas Sekar, Aditya Akella, Srinivasan Seshan, Ion Stoica, and Hui Zhang. 2013. Developing a predictive model of quality of experience for internet video. ACM SIGCOMM Computer Communication Review 43, 4 (2013), 339--350.

Digital Library

[3]

Yonghwan Bang, June-Koo Kevin Rhee, KyungSoo Park, Kyongchun Lim, Giyoung Nam, John D Shinn, Jongmin Lee, Sungmin Jo, Ja-Ryeong Koo, Jonggyu Sung, et al. 2016. CDN interconnection service trial: implementation and analysis. IEEE Communications Magazine 54, 6 (2016), 94--100.

Digital Library

[4]

Timm Böttger, Felix Cuadrado, Gareth Tyson, Ignacio Castro, and Steve Uhlig. 2018. Open connect everywhere: A glimpse at the internet ecosystem through the lens of the netflix cdn. ACM SIGCOMM Computer Communication Review 48, 1 (2018), 28--34.

Digital Library

[5]

Fei Chen, Cong Zhang, FengWang, and Jiangchuan Liu. 2015. Crowdsourced live streaming over the cloud. In 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, 2524--2532.

[6]

Michael L Fredman and Robert Endre Tarjan. 1987. Fibonacci heaps and their uses in improved network optimization algorithms. Journal of the ACM (JACM) 34, 3 (1987), 596--615.

Digital Library

[7]

Thiago Guarnieri, Idilio Drago, Alex B Vieira, Italo Cunha, and Jussara Almeida. 2017. Characterizing QoE in large-scale live streaming. In GLOBECOM 2017--2017 IEEE Global Communications Conference. IEEE, 1--7.

[8]

Fatima Haouari, Emna Baccour, Aiman Erbad, Amr Mohamed, and Mohsen Guizani. 2019. QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach. In ICC 2019--2019 IEEE International Conference on Communications (ICC). IEEE, 1--6.

[9]

Jian He, DiWu, Yupeng Zeng, Xiaojun Hei, and YonggangWen. 2013. Toward optimal deployment of cloud-assisted video distribution services. IEEE transactions on circuits and systems for video technology 23, 10 (2013), 1717--1728.

[10]

Xiangnan He and Tat-Seng Chua. 2017. Neural factorization machines for sparse predictive analytics. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 355--364.

Digital Library

[11]

https://www.businessofapps.com/data/twitch statistics/. 2020. Twitch Revenue and Usage Statistics.

[12]

https://www.chinainternetwatch.com/30115/kwai-dec 2019/. 2020. Kuaishou live broadcast DAU exceeded 100 million in 2019.

[13]

Alexis Huet and Dario Rossi. 2019. Demo Abstract: Explaining Web users? QoE with Factorization Machines. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 945--946.

[14]

Junchen Jiang, Rajdeep Das, Ganesh Ananthanarayanan, Philip A Chou, Venkata Padmanabhan, Vyas Sekar, Esbjorn Dominique, Marcin Goliszewski, Dalibor Kukoleca, Renat Vafin, et al. 2016. Via: Improving internet telephony call quality using predictive relay selection. In Proceedings of the 2016 ACM SIGCOMM Conference. 286--299.

Digital Library

[15]

Junchen Jiang, Vyas Sekar, Henry Milner, Davis Shepherd, Ion Stoica, and Hui Zhang. 2016. {CFA}: A practical prediction system for video qoe optimization. In 13th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 16). 137--150.

[16]

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

[17]

Roy Jonker and Anton Volgenant. 1987. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing 38, 4 (1987), 325--340.

Digital Library

[18]

Hongqiang Harry Liu, Raajay Viswanathan, Matt Calder, Aditya Akella, Ratul Mahajan, Jitendra Padhye, and Ming Zhang. 2016. Efficiently delivering online services over integrated infrastructure. In 13th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 16). 77--90.

[19]

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

[20]

Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh. 2019. Learning scheduling algorithms for data processing clusters. In Proceedings of the ACM Special Interest Group on Data Communication. 270--288.

Digital Library

[21]

Haitian Pang, Cong Zhang, Fangxin Wang, Han Hu, Zhi Wang, Jiangchuan Liu, and Lifeng Sun. 2018. Optimizing Personalized Interaction Experience in Crowd- Interactive Livecast: A Cloud-Edge Approach. In Proceedings of the 26th ACM international conference on Multimedia. 1217--1225.

Digital Library

[22]

Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International Conference on Data Mining. IEEE, 995--1000.

Digital Library

[23]

Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. Fast context-aware recommendations with factorization machines. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 635--644.

Digital Library

[24]

Yi Sun, Xiaoqi Yin, Junchen Jiang, Vyas Sekar, Fuyuan Lin, Nanshu Wang, Tao Liu, and Bruno Sinopoli. 2016. CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction. (2016), 272--285.

[25]

Feng Wang, Jiangchuan Liu, Minghua Chen, and Haiyang Wang. 2014. Migration towards cloud-assisted live media streaming. IEEE/ACM Transactions on networking 24, 1 (2014), 272--282.

[26]

FangxinWang, Cong Zhang, Jiangchuan Liu, Yifei Zhu, Haitian Pang, Lifeng Sun, et al. 2019. Intelligent edge-assisted crowdcast with deep reinforcement learning for personalized QoE. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 910--918.

[27]

Zhi Wang, Lifeng Sun, Chuan Wu, Wenwu Zhu, and Shiqiang Yang. 2014. Joint online transcoding and geo-distributed delivery for dynamic adaptive streaming. In IEEE INFOCOM 2014-IEEE Conference on Computer Communications. IEEE, 91--99.

[28]

Ting Yue, An-MingWei, Hong-BoWang, Xiang-Dong Deng, and Shi-Duan Cheng. 2016. A comprehensive data-driven approach to evaluating quality of experience on large-scale Internet video service. In 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 1479--1486.

[29]

Huaizheng Zhang, Han Hu, Guanyu Gao, Yonggang Wen, and Kyle Guan. 2018. DeepQoE: A unified framework for learning to predict video QoE. In 2018 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1--6.

[30]

Rui-Xiao Zhang, Tianchi Huang, Ming Ma, Haitian Pang, Xin Yao, Chenglei Wu, and Lifeng Sun. 2019. Enhancing the crowdsourced live streaming: a deep reinforcement learning approach. In Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. 55--60.

Digital Library

[31]

Rui-Xiao Zhang, Ming Ma, Tianchi Huang, Haitian Pang, Xin Yao, Chenglei Wu, Jiangchuan Liu, and Lifeng Sun. 2019. Livesmart: A QoS-Guaranteed Cost- Minimum Framework of Viewer Scheduling for Crowdsourced Live Streaming. In Proceedings of the 27th ACM International Conference on Multimedia. 420--428.

Digital Library

[32]

Rui-Xiao Zhang, Ming Ma, Tianchi Huang, Haitian Pang, Xin Yao, Chenglei Wu, and Lifeng Sun. 2020. A Practical Learning-based Approach for Viewer Scheduling in the Crowdsourced Live Streaming. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, 2s (2020), 1--22.

Digital Library

[33]

Xu Zhang, Siddhartha Sen, Daniar Kurniawan, Haryadi Gunawi, and Junchen Jiang. 2019. E2E: embracing user heterogeneity to improve quality of experience on the web. In Proceedings of the ACM Special Interest Group on Data Communication. 289--302.

Digital Library

[34]

Yifei Zhu, Jiangchuan Liu, Zhi Wang, and Cong Zhang. 2017. When cloud meets uncertain crowd: An auction approach for crowdsourced livecast transcoding. In Proceedings of the 25th ACM international conference on Multimedia. 1372--1380.

Digital Library

Cited By

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https://dl.acm.org/doi/10.5555/3691992.3692050
Li ZZhong PHuang JGao FWang J(2024)Achieving QoE Fairness in Bitrate Allocation of 360° Video StreamingIEEE Transactions on Multimedia10.1109/TMM.2023.327728626(1169-1178)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3277286
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cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

Copyright © 2020 ACM.

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Published: 12 October 2020

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Beijing Key Lab of Networked Multimedia
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National Key R&D Program of China

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MM '20

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MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

WA, Seattle, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Zhang RWang HShi SPang XPeng YXue ZLiu JBagchi SZhang Y(2024)Enhancing resource management of the world's largest PCDN system for on-demand video streamingProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692050(951-965)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692050
Li ZZhong PHuang JGao FWang J(2024)Achieving QoE Fairness in Bitrate Allocation of 360° Video StreamingIEEE Transactions on Multimedia10.1109/TMM.2023.327728626(1169-1178)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3277286
Huang SWang ZZhang ZZhang HWang XWang W(2024)Seer: Proactive Revenue-Aware Scheduling for Live Streaming Services in Crowdsourced Cloud-Edge PlatformsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621424(1801-1810)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621424
Huang TZhang RWu CSun LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Optimizing Adaptive Video Streaming with Human FeedbackProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611771(1707-1718)Online publication date: 26-Oct-2023
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Li CZhang RHuang TJia LSun LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Concerto: Client-server Orchestration for Real-Time Video AnalyticsProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611770(9215-9223)Online publication date: 26-Oct-2023
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Yuan ZRen KWang GMiao XChen HDuh WHuang HKato MMothe JPoblete B(2023)Hydrus: Improving Personalized Quality of Experience in Short-form Video ServicesProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591696(1127-1136)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591696
Zhang RYang CWang XHuang TWu CLiu JSun L(2023)Practical Cloud-Edge Scheduling for Large-Scale Crowdsourced Live StreamingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.326773134:7(2055-2071)Online publication date: Jul-2023
https://doi.org/10.1109/TPDS.2023.3267731
Chang ZChan S(2023)Bi-Criteria Approximation for a Multi-Origin Multi-Channel Auto-Scaling Live Streaming CloudIEEE Transactions on Multimedia10.1109/TMM.2022.315209325(2839-2850)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3152093
Zhang RHuang TWu CSun L(2023)Who is the Rising Star? Demystifying the Promising Streamers in Crowdsourced Live StreamingIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228881(1-10)Online publication date: 17-May-2023
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Zhang RYang CWang XHuang TWu CLiu JSun LMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)AggCast: Practical Cost-effective Scheduling for Large-scale Cloud-edge Crowdsourced Live StreamingProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3547807(3026-3034)Online publication date: 10-Oct-2022
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