New Cross-Domain QoE Guarantee Method Based on Isomorphism Flow

  • Zaijian WangEmail author
  • Chen Chen
  • Xinheng Wang
  • Lingyun Yang
  • Pingping Tang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)


This paper investigates the issue of Quality of Experience (QoE) for multimedia services over heterogeneous networks. A new concept of “Isomorphism Flow” (iFlow) was introduced for analyzing multimedia traffics, which is inspired by the abstract algebra based on experimental research. By using iFlow, the multimedia traffics with similar QoE requirements for different users are aggregated. A QoE evaluation method was also proposed for the aggregated traffics. Then a new cross-domain QoE guarantee method based on the iFlow QoE is proposed in this paper to adjust the network resource from the perspective of user perception. The proposed scheme is validated through simulations. Simulation results show that the proposed scheme achieves an enhancement in QoE performance and outperforms the existing schemes.


Diffserv networks Quality of experience Multimedia traffic 



This work was supported in part by National Natural Science Foundation of China (No. 61401004).


  1. 1.
    Karaadi, A., Sun, L., Mkwawa, I.-H.: Multimedia communications in Internet of Things QoT or QoE. In: 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 23–29 (2017)Google Scholar
  2. 2.
    Wang, Z., Dong, Y., Mao, S., Wang, X.: Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models. IEEE/CIC China Commun. 14(10), 202–218 (2017)CrossRefGoogle Scholar
  3. 3.
    Wang, Z., Mao, S., Yang, L., Tang, P.: A survey of multimedia big data. IEEE/CIC China Commun. 15(1), 155–176 (2018)CrossRefGoogle Scholar
  4. 4.
    Naserian, E., Wang, X., Dahal, K., Wang, Z., Wang, Z.: Personalized location prediction for group travellers from spatial-temporal trajectories. Future Gener. Comput. Syst. 24(1), 1–15 (2018)Google Scholar
  5. 5.
    Wang, W., Wang, Q.: Price the QoE, not the data: SMP-economic resource allocation in wireless multimedia Internet of Things. IEEE Commun. Mag. 56(9), 74–79 (2018)CrossRefGoogle Scholar
  6. 6.
    Li, M., Si, P., Zhang, Y.: Delay-tolerant data traffic to software-defined vehicular networks with mobile edge computing in smart city. IEEE Trans. Veh. Technol. (Early Access) 67, 1–14 (2018)CrossRefGoogle Scholar
  7. 7.
    He, X., Wang, K., Huang, H., Miyazaki, T., Wang, Y., Guo, S.: Green resource allocation based on deep reinforcement learning in content-centric IoT. IEEE Trans. Emerg. Top. Comput. (Early Access), 1 (2018)Google Scholar
  8. 8.
    Nightingale, J., Salva-Garcia, P., Calero, J.M.A., Wang, Q.: 5G-QoE: QoE modeling for ultra-HD video streaming in 5G networks. IEEE Trans. Broadcast. 64(2), 621–634 (2018)CrossRefGoogle Scholar
  9. 9.
    Charonyktakis, P., Plakia, M., Tsamardinos, I., Papadopouli, M.: On user-centric modular QoE prediction for VoIP based on machine-learning algorithms. IEEE Trans. Mob. Comput. 15(6), 1443–1456 (2016)CrossRefGoogle Scholar
  10. 10.
    Murray, N., Lee, B., Qiao, Y., Miro-Muntean, G.: The impact of scent type on olfaction-enhanced multimedia quality of experience. IEEE Trans. Syst. Man Cybern.: Syst. 47(9), 2503–2515 (2017)Google Scholar
  11. 11.
    Tao, X., Dong, L., Li, Y., Zhou, J., Ge, N., Jianhua, L.: Real-time personalized content catering via viewer sentiment feedback: a QoE perspective. IEEE Netw. 29(6), 14–19 (2015)CrossRefGoogle Scholar
  12. 12.
    Scott, M.J., Guntuku, S.C., Lin, W., Ghinea, G.: Do personality and culture influence perceived video quality and enjoyment. IEEE Trans. Multimed. 18(9), 1796–1807 (2016)CrossRefGoogle Scholar
  13. 13.
    Mitra, K., Zaslavsky, A., Åhlund, C.: Context-aware QoE modelling, measurement, and prediction in mobile computing systems. IEEE Trans. Mob. Comput. 14(5), 920–936 (2015)CrossRefGoogle Scholar
  14. 14.
    Chen, B.-W., Ji, W., Jiang, F., Rho, S.: QoE-enabled big video streaming for large-scale heterogeneous clients and networks in smart cities. IEEE Access 4(1), 97–107 (2016)CrossRefGoogle Scholar
  15. 15.
    Chen, Y., Kaishun, W., Zhang, Q.: From QoS to QoE: a tutorial on video quality assessment. IEEE Commun. Surv. Tutor. 7(2), 1126–1165 (2015)CrossRefGoogle Scholar
  16. 16.
    ITU-T Recommendation P.913. Methods for the subjective assessment of video quality, audio quality and audiovisual quality of Internet video and distribution quality television in any environment (2014)Google Scholar
  17. 17.
    ITU-R Recommendation BT.500-11. Methodology for the subjective assessment of the quality of television pictures (2002)Google Scholar
  18. 18.
    Takahashi, A., Hands, D., Barriac, V.: Standardization activities in the ITU for a QoE assessment of IPYV. IEEE Commun. Mag. 46(2), 78–84 (2008)CrossRefGoogle Scholar
  19. 19.
    Wang, Z.J., Dong, Y.N., Wang, X.: A dynamic service class mapping scheme for different QoS domains using flow aggregation. IEEE Syst. J. 9(4), 1299–1310 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Anhui Normal UniversityWuhuChina
  2. 2.University of West LondonLondonUK

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