Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

Data-Driven QoE Measurement

  • Xiaoming He
  • Kun Wang
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_89-1



Recently, the strict definition of data-driven QoE measurement has not been given. The International Telecommunication Union (ITU-T) has defined the QoE concept as the entire thing of availability of services subjectively perceived by end users. The definition of QoE by the European Qualinet is the degree of satisfaction or annoyance of the end users of services because the utility and/or the expectations regarding services are based on end-user attitudes and current service situations (He et al., 2018). Furthermore, data-driven QoE measurement can be defined from the perspective of objective-based or subjective-based metrics. Generally speaking, data-driven QoE measurement is the...

This is a preview of subscription content, log in to check access.


  1. Canbaz M, Thom J, Gunes MH (2017) Comparative analysis of internet topology data sets. In: Proceedings of 2017 IEEE conference on computer communications workshops (INFOCOM WKSHPS)Google Scholar
  2. Chen K, Shen H (2015) Maximizing P2P file access availability in mobile ad hoc networks though replication for efficient file sharing. IEEE Trans Comput 64(4):1029–1042MathSciNetCrossRefGoogle Scholar
  3. Du M, Wang K, Xia Z, Zhang Y (2018) Differential privacy preserving of training model in wireless big data with edge computing. IEEE Trans Big Data.  https://doi.org/10.1109/TBDATA.2018.2829886
  4. Fang Q, Wang J, Gong Q (2016) QoS-driven power management of data centers via model predictive control. IEEE Trans Auto Sci Eng 13(4):1557–1566CrossRefGoogle Scholar
  5. He X, Wang K, Huang H, Miyazaki T, Wang Y, Guo S (2018) Green resource allocation based on deep reinforcement learning in content-centric IoT. IEEE Trans Emerg Top Comput.  https://doi.org/10.1109/TETC.2018.2805718
  6. He X, Wang K, Huang H, Miyazaki T, Wang Y, Sun Y (2018) QoE-driven joint resource allocation for content delivery in fog computing environment. In Proceedings of ICC.Google Scholar
  7. Jiang J, Sekar V, Milner H, Shepherd D, Stoica I, Zhang H (2016a) CFA: a practical prediction system for video QoE optimization. In: Proceedings of 13th USENIX symposium on networked systems design and implementation (NSDI’16)Google Scholar
  8. Jiang J, Sun S, Sekar V, Zhang H (2017) Pytheas: enabling data-driven quality of experience optimization using group-based exploration-exploitation. In: Proceedings of 14th USENIX symposium on networked systems design and implementation (NSDI’17)Google Scholar
  9. Purohit L, Kumar S (2018) A classification based web service selection approach. IEEE Trans Serv Comput.  https://doi.org/10.1109/TSC.2018.2805352
  10. Sterca A, Hellwagner H, Florian Boian F, Vancea A (2016) Media-friendly and TCP-friendly rate control protocols for multimedia streaming. IEEE Trans Circ Syst Video Technol 26(8):1516–1531CrossRefGoogle Scholar
  11. Usman M, Yang N, Jan M, He X, Xu M, Lam k (2018) A joint framework for QoS and QoE for video transmission over wireless multimedia sensor networks. IEEE Trans Mob Comput 17(4):746–759Google Scholar
  12. Wang K, Gao H, Xu X, Jiang J, Yue D (2016) An energy-efficient reliable data transmission scheme for complex environmental monitoring in underwater acoustic sensor networks. IEEE Sens J 16(11):4051–4062CrossRefGoogle Scholar
  13. Wang K, Mi J, Xu C, Zhu Q, Shu L, Deng DJ (2016) Real-time load reduction in multimedia big data for mobile Internet. ACM Trans Mult Comput Commun App 12(5):76Google Scholar
  14. Wang K, Zhuo L, Shao Y, Yue D, Tsang KF (2016) Towards distributed data processing on intelligent leakpoints prediction in petrochemical industries. IEEE Trans Ind Informatics 12(6):2091–2102CrossRefGoogle Scholar
  15. Wang Y, He D, Ding L, Zhang W, Li W, Wu Y, Liu N, Wang Y (2017b) Media transmission by cooperation of cellular network and broadcasting network. IEEE Trans Broad 63(3):571–576CrossRefGoogle Scholar
  16. Wang K, Zhou Q, Guo S, Luo J (2018) Cluster frameworks for efficient scheduling and resource allocation in data center networks: a survey. IEEE Commun Surv Tutor 20(4):3560–3580CrossRefGoogle Scholar
  17. Wang Y, Wang* K, Huang H, Miyazaki T, Guo S (2019) Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications. IEEE Transactions on Industrial Informatics. IEEE Trans Ind Informatics 15(2):976–986Google Scholar
  18. Xu C, Wang K, Li L, Xia R, Guo S, Guo M (2018) Renewable energy-aware big data analytics in geo-distributed data centers with reinforcement learning. IEEE Trans Netw Sci Eng.  https://doi.org/10.1109/TNSE.2018.2813333
  19. Zhang J, Wang Z, Wang K, Guo S, Guo M (2017) Improving power efficiency for online video streaming service: a self-adaptive approach. IEEE Trans Sust Comput.  https://doi.org/10.1109/TSUSC.2017.2739798

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xiaoming He
    • 1
  • Kun Wang
    • 2
  1. 1.College of IoTNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of CaliforniaLos AngelesUSA

Section editors and affiliations

  • Song Guo
    • 1
  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityKowloonHong Kong