Quality Assessment and Measurement for Internet Video Streaming

doi:10.12142/ZTECOM.201901003

ZTE Communications ›› 2019, Vol. 17 ›› Issue (1): 12-17.DOI: 10.12142/ZTECOM.201901003

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Quality Assessment and Measurement for Internet Video Streaming

ZHANG Xinggong, XIE Lan, GUO Zongming

Peking University, Beijing 100871, China

Received:2018-09-04 Online:2019-02-20 Published:2019-11-14
About author:ZHANG Xinggong (zhangxg@pku.edu.cn) received the Ph.D. degree from Department of Computer Science, Peking University, China in 2011. He has been an associate professor with the Institute of Computer Science and Technology of Peking University since 2012. His research interests lie in multimedia networks, video communications, information-centric network, and dynamic adaptive HTTP streaming over HTTP (DASH). He was a senior researcher at Founder R&D Center, Beijing, China from 1998 to 2007, and a visiting scholar at the Polytechnic Institute of New York University, USA from 2010 to 2011.|XIE Lan received her master’s degree from Department of Computer Science, Peking University, China, and currently is a researcher at Hulu, China. Her research interests lies in multimedia streaming, adaptive video streaming, and panoramic video streaming.|GUO Zongming received the bachelor, master and Ph.D. degrees from Department of Computer Science, Peking University, China in 1987, 1990 and 1994, respectively. He has been a professor with the Institute of Computer Science and Technology of Peking University since 2002. His research interests lie in multimedia streaming, image/video compression, image and video retrieval, watermarking, IPTV, and mobile multimedia. He is a member of the Institute of Electrical and Electronics Engineers (IEEE), a senior member of The Society of Motion Picture and Television Engineers (SMPTE) for China, and a senior member of China Computer Federation (CCF).
Supported by:
This work was supported by National Key R&D Program of China(No. 2018YFB0803702);Beijing Culture Development Funding under Grant(No.2016-288);Toutiao Funding(No. ZN20171224003)

Abstract

Abstract:

Benefiting from the improvements of Internet infrastructure and video coding technology, online video services are becoming a new favorite form of video entertainment. However, most of the existing video quality assessment methods are designed for broadcasting/cable televisions and it is still an open issue how to assess and measure the quality of online video services. In this paper, we survey the state-of-the-art video streaming technologies, and present a framework of quality assessment and measurement for Internet video streaming. This paper introduces several metrics for user’s quality of experience (QoE). These QoE metrics are classified into two categories: objective metrics and subjective metrics. It is different for service participators to measure objective and subjective metrics. The QoE measurement methodologies consist of client-side, server-side, and in-network measurement.

Key words: Internet video streaming, QoE, QoE assessment and measurement, HTTP adaptive streaming

ZHANG Xinggong, XIE Lan, GUO Zongming. Quality Assessment and Measurement for Internet Video Streaming[J]. ZTE Communications, 2019, 17(1): 12-17.

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References 17

[1]	China Internet Network Information Center. The 40th CNNIC Statistical Survey Report on Internet Development in China [R]. Aug. 2017
[2]	ITU-T. Vocabulary for Performance and Quality of Service: Recommendation P.10/G.100-Amendment 3[S]. 2012
[3]	KRISHNAN S S, SITARAMAN R K . Video Stream Quality Impacts Viewer Behavior: Inferring Causality Using Quasi-Experimental Designs[J]. ACM Transactions on Networking, 2013,21(6):2001-2014. DOI: 10.1109/tnet.2013.2281542
[4]	HOSSFELD T, EGGER S, SCHATZ R , et al. Initial Delay vs. Interruptions: Between the Devil and the Deep Blue Sea [C]//Fourth International Workshop on Quality of Multimedia Experience, Yarra Valley, Australia, 2012: 1-6. DOI: 10.1109/QoMEX.2012.6263849
[5]	HOßFELD T, SEUFERT M, HIRTH M , et al. Quantification of YouTube QoE Via Crowdsourcing [C]//IEEE International Symposium on Multimedia, Dana Point, USA, 2011: 494-499. DOI: 10.1109/ISM.2011.87
[6]	QI Y N, DAI M Y . The Effect of Frame Freezing and Frame Skipping on Video Quality [C]//International Conference on Intelligent Information Hiding and Multimedia, Pasadena, CA, USA, 2006: 423-426. DOI: 10.1109/IIH-MSP.2006.265032
[7]	LIU Y T, YUN S, MAO Y N , et al. A Study on Quality of Experience for Adaptive Streaming Service [C]//IEEE International Conference on Communications Workshops (ICC), Budapest, Hungary, 2013: 682-686. DOI: 10.1109/ICCW.2013.6649320
[8]	CHERIF W, KSENTINI A, NÉGRU D , et al. A-SQA: Efficient Real-Time Video Streaming QoE Tool in a Future Media Internet Context [C]//IEEE International Conference on Multimedia and Expo, Barcelona, Spain, 2011: 1-6. DOI: 10.1109/ICME.2011.6011993
[9]	MOK R K P, CHAN E W W, CHANG R K C . Measuring the Quality of Experience of HTTP Video Streaming [C]//12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops, Dublin, Ireland, 2011: 485-492. DOI: 10.1109/INM.2011.5990550
[10]	CLAEYS M, LATRE S, FAMAEY J , et al. Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client[J]. IEEE Communications Letters, 2014,18(4):716-719. DOI: 10.1109/lcomm.2014.020414.132649 DOI URL
[11]	YIN X Q, JINDAL A, SEKAR V , et al. A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP[J]. ACM SIGCOMM Computer Communication Review, 2015,45(5):325-338. DOI: 10.1145/2829988. 2787486
[12]	YANG F Z, WAN S . Bitstream-Based Quality Assessment for Networked Video: A Review[J]. IEEE Communications Magazine, 2012,50(11):203-209. DOI: 10.1109/mcom.2012.6353702
[13]	STAEHLE B, HIRTH M, PRIES R , et al. YoM: A YouTube Application Comfort Monitoring Tool[R]. Berlin, Germany: Inst.Informatik, Tech. Rep. 467, 2010
[14]	DALAL A C and PERRY E . A New Architecture for Measuring and Assessing Streaming Media Quality [C]//3rd Workshop on Passive and Active Measurement Workshop (PAM), La Jolla, USA, 2003: 223-231
[15]	JULURI P, PLISSONNEAU L, ZENG Y , et al. Viewing YouTube from a Metropolitan Area: What do Users Accessing from Residential ISPs Experience? [C]//IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), Ghent, Belgium, 2013: 589-595
[16]	MAZHAR M H, SHAFIQ Z . Real-Time Video Quality of Experience Monitoring for HTTPS and QUIC [C]//IEEE INFOCOM 2018—IEEE Conference on Computer Communications, Honolulu, USA, 2018: 1331-1339. DOI: 10.1109/INFOCOM.2018.8486321
[17]	MANGLA T, HALEPOVIC E, AMMAR M , et al. MIMIC: Using Passive Network Measurements to Estimate HTTP-Based Adaptive Video QoE Metrics [C]//Network Traffic Measurement and Analysis Conference (TMA), Dublin, Ireland, 2017: 1-6. DOI: 10.23919/TMA.2017.8002920

Quality Assessment and Measurement for Internet Video Streaming

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