Dynamic Adaptive Streaming over HTTP (DASH) has been widely adopted to deal with such user diversity as network conditions and device capabilities. In DASH systems, the computation-intensive transcoding is the key technology to enable video rate adaptation, and cloud has become a preferred solution for massive video transcoding. Yet the cloud-based solution has the following two drawbacks. First, a video stream now has multiple versions after transcoding, which increases the network traffic traversing the core network. Second, the transcoding strategy is normally fixed and thus is not flexible to adapt to the dynamic change of viewers. Considering that mobile users, who normally experience dynamic network conditions from time to time, have occupied a very large portion of the total users, adaptive wireless transcoding is of great importance. To this end, we propose an adaptive wireless video transcoding framework based on the emerging edge computing paradigm by deploying edge transcoding servers close to base stations. With this design, the core network only needs to send the source video stream to the edge transcoding server rather than one stream for each viewer, and thus the network traffic across the core network is significantly reduced. Meanwhile, our edge transcoding server cooperates with the base station to transcode videos at a finer granularity according to the obtained users' channel conditions, which smartly adjusts the transcoding strategy to tackle with time-varying wireless channels. In order to improve the bandwidth utilization, we also develop efficient bandwidth adjustment algorithms that adaptively allocate the spectrum resources to individual mobile users. We validate the effectiveness of our proposed edge computing based framework through extensive simulations, which confirm the superiority of our framework.

We study the energy harvesting problem in the Internet of Things with heterogeneous users, where there are three types of single-antenna users: ID users that only receive information, EH users that can only receive energy, and ID/EH users that receive information and energy simultaneously from a multiantenna base station via power splitting. We aim to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the ID users and ID/EH users by jointly designing the power allocation at the transmitter and the power splitting strategy at the ID/EH receivers under the maximum transmit power and the minimum energy harvesting constraints. Specifically, we first apply the semidefinite relaxation (SDR), zero-forcing (ZF), and maximum ratio transmission (MRT) techniques to solve the nonconvex problems. We then apply the zero-forcing dirty paper coding (ZF-DPC) technique to eliminate the multiuser interference and derive the closed-form optimal solution. Numerical results show that ZF-DPC provides higher achievable minimum SINR than SDR and ZF in most cases.