New Cross-Domain QoE Guarantee Method Based on Isomorphism Flow

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.

[1]  Keshav P. Dahal,et al.  Personalized location prediction for group travellers from spatial-temporal trajectories , 2018, Future Gener. Comput. Syst..

[2]  Yanjiao Chen,et al.  From QoS to QoE: A Tutorial on Video Quality Assessment , 2015, IEEE Communications Surveys & Tutorials.

[3]  Shiwen Mao,et al.  Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models , 2017, China Communications.

[4]  Gabriel-Miro Muntean,et al.  The Impact of Scent Type on Olfaction-Enhanced Multimedia Quality of Experience , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Yanhua Zhang,et al.  Delay-Tolerant Data Traffic to Software-Defined Vehicular Networks With Mobile Edge Computing in Smart City , 2018, IEEE Transactions on Vehicular Technology.

[6]  Maria Papadopouli,et al.  On User-Centric Modular QoE Prediction for VoIP Based on Machine-Learning Algorithms , 2016, IEEE Transactions on Mobile Computing.

[7]  Yang Li,et al.  Real-time personalized content catering via viewer sentiment feedback: a QoE perspective , 2015, IEEE Network.

[8]  Shiwen Mao,et al.  A survey of multimedia big data , 2018, China Communications.

[9]  Is-Haka Mkwawa,et al.  Multimedia Communications in Internet of Things QoT or QoE? , 2017, 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).

[10]  Wei Wang,et al.  Price the QoE, Not the Data: SMP-Economic Resource Allocation in Wireless Multimedia Internet of Things , 2018, IEEE Communications Magazine.

[11]  James Nightingale,et al.  5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks , 2018, IEEE Transactions on Broadcasting.

[12]  Vincent Barriac,et al.  Standardization activities in the ITU for a QoE assessment of IPTV , 2008, IEEE Communications Magazine.

[13]  Weisi Lin,et al.  Do Personality and Culture Influence Perceived Video Quality and Enjoyment? , 2016, IEEE Transactions on Multimedia.

[14]  Seungmin Rho,et al.  QoE-Enabled Big Video Streaming for Large-Scale Heterogeneous Clients and Networks in Smart Cities , 2016, IEEE Access.

[15]  Yuning Dong,et al.  A Dynamic Service Class Mapping Scheme for Different QoS Domains Using Flow Aggregation , 2015, IEEE Systems Journal.

[16]  Song Guo,et al.  Green Resource Allocation Based on Deep Reinforcement Learning in Content-Centric IoT , 2018, IEEE Transactions on Emerging Topics in Computing.

[17]  Jemal H. Abawajy,et al.  An efficient replicated data access approach for large-scale distributed systems , 2004, IEEE International Symposium on Cluster Computing and the Grid, 2004. CCGrid 2004..

[18]  Arkady B. Zaslavsky,et al.  Context-Aware QoE Modelling, Measurement, and Prediction in Mobile Computing Systems , 2015, IEEE Transactions on Mobile Computing.