Multimedia Internet-of-Things (IoT) systems have been widely used in surveillance, automatic behavior analysis and event recognition, which integrate image processing, computer vision and networking capabilities. In conventional multimedia IoT systems, videos captured by surveillance cameras are required to be delivered to remote IoT servers for video analysis. However, the long-distance transmission of a large volume of video chunks may cause congestions and delays due to limited network bandwidth. Nowadays, mobile devices, e.g., smart phones and tablets, are resource-abundant in computation and communication capabilities. Thus, these devices have the potential to extract features from videos for the remote IoT servers. By sending back only a few video features to the remote servers, the bandwidth starvation of delivering original video chunks can be avoided. In this paper, we propose an edge computing framework to enable cooperative processing on resource-abundant mobile devices for delay-sensitive multimedia IoT tasks. We identify that the key challenges in the proposed edge computing framework are to optimally form mobile devices into video processing groups and to dispatch video chunks to proper video processing groups. Based on the derived optimal matching theorem, we put forward a cooperative video processing scheme formed by two efficient algorithms to tackle above challenges, which achieves sub-optimal performance on the human detection accuracy. The proposed scheme has been evaluated under diverse parameter settings. Extensive simulation confirms the superiority of the proposed scheme over other two baseline schemes.

Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. But the design of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground devices presents a conundrum. Most existing adaptive bitrate (ABR) algorithms are not optimized for the UAV links, which usually fluctuate dramatically due to the dynamic flight states of the UAV. In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs. By incorporating the inherent sensor data with network observations, SA-ABR trains a deep reinforcement learning (DRL) model to extract salient features from the flight state information and automatically learn ABR algorithm to adapt to the varying UAV channel capacity through the training process. SA-ABR does not rely on any assumptions or models about UAV's flight states or the environment, but instead, it makes decisions by exploiting temporal properties of past throughput patterns through the long short-term memory (LSTM) to adapt itself to a wide range of highly dynamic environments. We have implemented SA-ABR in a commercial UAV and evaluated it in the wild. We compare SA-ABR with a variety of existing state-of-the-art ABR algorithms, and the results show that our system outperforms the best known existing ABR algorithm by 21.4% in terms of the average quality of experience (QoE) reward.

Unmanned aerial vehicle (UAV) has received tremendous attention in the area of edge computing due to its flexible deployment and wide coverage accessibility. In weak infrastructure scenarios, multiple UAVs can form on-site edge computing clusters to handle the real-time tasks. Further, a multi-UAV enabled edge-cloud computing system is coined by cooperating the UAVs with remote cloud, which provides superior computing capability. However, the uneven distribution of tasks makes it difficult to meet the real-time requirements when load balancing is unavailable. To address above issue, a delay minimization problem for multi-UAV enabled edge-cloud cooperative offloading is investigated in this paper. The problem is formulated as a non-convex problem based on models that reflect characteristics of the system, such as ubiquitous network congestion, air-to-ground wireless channel and cooperative parallel computing. An efficient cooperative offloading algorithm is proposed to address the problem. Specifically, convex approximation is applied to make the original problem tractable, and Lyapunov optimization is utilized to make online task offloading decisions. Finally, the correctness of the models are verified through a practical UAV-edge computing platform. Simulations based on measurement results and real-world datasets indicate that, the proposed algorithm fully utilizes the available energy to significantly reduce the tasks' completion delay.

An important issue in an edge computing (EC) network is to increase the utilities of the end users concurrently accessing the computation resources. In this paper, we consider the task offloading in EC-enabled networks where the end users efficiently utilize the dispersed computation and communication resources in a multi-path multi-hop manner. We propose a binary optimization framework that generalizes multi-hop wireless EC task offloading as jointly making decisions of server selecting and traffic routing in networks of arbitrary topology (JoSRAT). We further develop an approximation algorithm JoSRAT that enables for a fully distributed implementation together with the worst-case performance guarantees. Interestingly, our proposed distributed algorithm achieves nearly optimal in the numerical evaluations, significantly outperforming the worst-case guarantees. The proposed algorithm also outperforms a widely-used heuristic, i.e., First Fit, in terms of computational time complexity, indicating the superior capability of the proposed framework.