With the advances in hand-held devices (smart-phones and tablets, etc.) and high speed wireless networks, users have an explosive growth demand for live streaming service. Due to the diversity of user equipments (UEs), the live streaming has to be transcoded into different versions. However, transcoding is a computationally expensive and time consuming process. Due to the dynamic characteristics of wireless network environment, providing high quality and low latency live videos for UEs is a big challenge. This study investigates user scheduling, transcoding decision, computational and wireless spectrum resources allocation problem in edge-clouds aided heterogeneous networks (Het-Nets). The research focuses on improving UEs' quality of service (QoS) for live-streaming services, which includes quality of video and latency requirements. Different from existing literature, to approach the real wireless environment, the available computa- tional and wireless spectrum resources are modeled as random processes in the work. Considering dynamic characteristics of wireless networks and available resources, the above problem is modeled as a Markov decision process (MDP). Since the action space of the MDP is multi-dimensional continuous variables mixed with discrete variables, it is difficult to solve this problem by traditional learning algorithms. Therefore, an enhanced actor-critic algorithm is proposed to resolve the problem, in which both the actor part and the critic part employ eligibility traces. Extensive simulation results with different system parameters show the effectiveness of the proposed algorithm.

With the rapid development of Internet of Things (IoT), distributed denial of service (DDoS) attacks become the important security threat of the IoT. Characteristics of IoT, such as large quantities and simple function, which have easily caused the IoT devices or servers to be attacked and be turned into botnets for launching DDoS attacks. In this paper, we use software-defined networking (SDN) to develop moving target defense (MTD) architecture that increases uncertainty because of ever changing attack surface. In addition, we deploy SDN-based honeypots to mimic IoT devices, luring attackers and malwares. Finally, experimental results show that combination of MTD and SDN-based honeypots can effectively hide network asset from scanner and defend against DDoS attacks in IoT.