Driven by the large-scale video traffic, mobile edge computing (MEC) has emerged as a promising technique that extends cloud-computing capabilities to the proximate small base stations (SBSs) in wireless networks, especially in ultra-dense networks (UDNs). With MEC, video transcoding, which processes the adaptive bitrates of a video and provides the adaptive video streaming to users, can significantly release the backhaul burden of networks. However, video transcoding is a time-consuming task, and how to guarantee quality-of- service (QoS) for large video data with MEC is still challenging. To address this issue, in this paper, we propose a joint SBSs selection, tasks scheduling, and resource allocation approach for achieving a delay- optimal transcoding under the constraints of network cost. Specifically, to reduce the delay, a set of SBSs are formed into a Virtual SBSs Group (VSG) to perform the video transcoding and delivering in parallel for a given user. Then, the joint tasks scheduling and feasible resource allocation are performed to minimizing total delay while maintaining a low network cost. The optimization problem is formulated as a mixed integer non- convex programming problem and a three-stage search solution is proposed to solve it. Simulation results show that our proposed approach can significantly improve the transcoding performance while satisfying the resource consumption constraint.

Mobile edge computing, Non-orthogonal multiple access (NOMA), Resource allocation, Ultra-dense network, Video transcoding
2018 IEEE International Conference on Communications, ICC 2018
School of Information Technology

Liu, Y. (Yiming), Yu, F.R, Li, X. (Xi), Ji, H. (Hong), Zhang, H. (Heli), & Leung, V.C.M. (Victor C. M.). (2018). Joint Access and Resource Management for Delay-Sensitive Transcoding in Ultra-Dense Networks with Mobile Edge Computing. In IEEE International Conference on Communications. doi:10.1109/ICC.2018.8422662