Joint Caching Allocation and Delay Control for Network Slices Carrying Multimedia Services in 5G Core Network As one of the most important technologies, network slicing (NS) has received extensive attention and research efforts in recent years. The infrastructure providers (InP) can divide the physical network into multiple logical networks by NS, and the service provider (SP) can rent these NS instances from the InP to meet their own requirements. Since most multimedia services are usually QoSaware, such as virtual reality (VR), mobile highdefinition video and so on, caching resource can be allocated to different NS instances on demand to reduce the transmission delay and improving the quality of experience (QoE) of endusers. Due to the limited amount of caching resource, the issue about how to efficiently allocate caching resource to increase network revenues and save expenses becomes very critical for InPs. To achieve this goal, this paper proposes a delayconstrained caching resource allocation scheme based on Benders decomposition algorithm. It can maximize the revenues of InPs while meeting the service requirement of each network slice. Finally, extensive numerical simulations are executed to verify the performance of the proposed scheme.

QoEDriven Resource Allocation Optimized for DelaySensitive VR Video Uploading over Cellular Network Uploading Virtual Reality (VR) video over cellular networks is expected to boom in near future, as general consumers could generate highquality VR videos with portable 360degree cameras and are willing to share with others. Consequently, concerns of uplink bandwidth and delay arose for current popular technology of tilebased VR video streaming, which requires high quality video to transcode into multiple representations for further adaptive streaming. Motivated by this, we proposed a novel scheme for uplink delivery of tilebased VR video over cellular network, in which encoding bit rate of each tile is determined by uplink resource allocation (RA), and quality of content (QoC) contribution of each tile and channel quality of user equipments (UEs) are jointly considered during RA. Moreover, the RA problem is formulated as a frequency and time dependent nondeterministic polynomial(NP)hard problem, which can be effectively solved by our proposed approximate convex algorithm. Simulation results show that the proposed algorithm can achieve higher utility, that is higher total quality of experience (QoE) for viewers.