Optimizing Live Layered Video Multicasting Over LTE With Mobile Edge Computing

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 10; pp. 12072 - 12084
• Main Authors: Hwang, Ren-Hung, Wang, Chih-Yu, Hwang, Jenq-Neng, Lin, Yu-Ren, Chen, Wei-Yu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bandwidths; Computational geometry; Convex optimization; Convexity; Digital media; Edge computing; Error correction; Forward Error Correction (FEC); Heuristic methods; Live video streaming; Long Term Evolution; Mobile computing; Mobile Edge Computing (MEC); Multicast communication; Multicasting; Optimization; Quality of experience; Quality of Experience (QoE); Radio; Resource management; Servers; Static VAr compensators; Streaming media; User satisfaction; Video transmission; Wireless communications; Wireless networks
• DOI: 10.1109/TVT.2020.3011633
• ISSN: 0018-9545

Live video streaming has become one of the key applications in mobile wireless networks. To offload the bandwidth requirement in both backhaul and radio access networks, the integration of Mobile Edge Computing (MEC) and multicasting have become a natural candidate. However, less attention has been paid to the user Quality of Experience-driven (QoE-driven) approach to optimize the radio resource management of multicasting in mobile wireless networks. In this work, we study the optimal radio resource management, including modulation and coding scheme (MCS) selection, radio resource blocks allocation, and Forward Error Correction (FEC), for multicasting in LTE networks with the assistance of MEC. We formulate it as a convex optimization problem and propose a weighted sub-gradient (WSG) method to find the near-optimal solution. In addition, we also propose a heuristic algorithm based on the concept of Maximizing marginal Gain and Minimizing marginal Loss (MGML). Our simulation results show that both approaches are able to achieve near-optimal solutions and outperform previous work, including MSML <xref ref-type="bibr" rid="ref11">[11] and OLM <xref ref-type="bibr" rid="ref14">[14] . Our simulation results also show that WSG yields the best QoE fairness index while MGML yields the best system utility in most scenarios.