QoE-Aware Energy Efficient Hierarchical Small Cell Deployment for Multimedia IoT Services

Traffic proliferation induced by mobile video streaming has been evoking tremendous challenges to resource management in 5G Internet-of-Things (IoT) networks. Although small cell based heterogeneous network is capable of traffic offloading, it still requires a systemic deployment with optimal allocation and resource management to deal with high data rates and network densification of mobile video streaming users. In this paper, we propose a two-tier hierarchical small cell based heterogeneous network infrastructure to tackle the aforementioned challenges. In particular, we develop an offline tier for the long-term small cell allocation, formulated with energy efficiency and predicted quality of experience (QoE) maximization. We also propose an online tier, characterizing the real-time on-demand network-slicing based small cell deployment, to optimize the dynamic QoE of users. Simulation results show that the proposed scheme achieves optimal energy efficiency and real-time improved QoE performance simultaneously, for different scale of heterogeneous IoT networks with dynamic mobile video streaming requirements by users, and clear advantages in highly dense networks with high percentage of video streaming services in IoT HetNets.

[1]  Ayman Radwan,et al.  Integer-Based Multi-Objective Algorithm for Small Cell Allocation Optimization , 2020, IEEE Communications Letters.

[2]  Abd-Elhamid M. Taha,et al.  Dynamic Clustering for Power Effective Small Cell Deployment in HetNet 5G Networks , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[3]  Lea Skorin-Kapov,et al.  A Framework for in-Network QoE Monitoring of Encrypted Video Streaming , 2020, IEEE Access.

[4]  Celestine Iwendi,et al.  Optimal Cooperative Offloading Scheme for Energy Efficient Multi-Access Edge Computation , 2020, IEEE Access.

[5]  Luigi Atzori,et al.  QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey , 2019, IEEE Communications Surveys & Tutorials.

[6]  Xi Fang,et al.  K-means-based channel equalization method for polarization-division-multiplexed optical OFDM/OQAM systems , 2019, AIIPCC '19.

[7]  Jian Bai,et al.  Network Slicing Technique Assisted Resource Allocation in Small-Cell Networks , 2019, 2019 IEEE 19th International Conference on Communication Technology (ICCT).

[8]  Mostafa Ammar,et al.  Using Session Modeling to Estimate HTTP-Based Video QoE Metrics From Encrypted Network Traffic , 2019, IEEE Transactions on Network and Service Management.

[9]  Iordanis Koutsopoulos,et al.  Jointly Optimizing Content Caching and Recommendations in Small Cell Networks , 2019, IEEE Transactions on Mobile Computing.

[10]  Sujit Dey,et al.  Quality of Service Optimization for Vehicular Edge Computing with Solar-Powered Road Side Units , 2018, 2018 27th International Conference on Computer Communication and Networks (ICCCN).

[11]  Lea Skorin-Kapov,et al.  YouTube QoE Estimation from Encrypted Traffic: Comparison of Test Methodologies and Machine Learning Based Models , 2018, 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX).

[12]  Marius Pesavento,et al.  Optimized Cell Planning for Network Slicing in Heterogeneous Wireless Communication Networks , 2018, IEEE Communications Letters.

[13]  Kim Fung Tsang,et al.  Efficiency and robustness management for IEEE 802.15.4 in healthcare sensor network , 2015, IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society.

[14]  Ayman Radwan,et al.  Multi-Objective Optimization of Green Small Cell Allocation for IoT Applications in Smart City , 2020, IEEE Access.

[15]  Marta Solera,et al.  A Network-Layer QoE Model for YouTube Live in Wireless Networks , 2019, IEEE Access.