Most camera-based mobile devices require ultra low-latency video analytics such as object
detection and action recognition. These devices face severe resource constraints, and thus …

By 2022, videos will account for 82% of global Internet traffic. Many camera-based mobile
devices, though with limited resources, require ultra low-latency video analytics such as …

Videos are projected to account for roughly 80% of global mobile data traffic by 2028. Many
camera-equipped mobile devices, such as surveillance drones, require realtime video …

Large bandwidth, Low latency and intensive computing are the main challenge in high-
performance virtual reality (VR) video transmission. As mobile edge computing (MEC) can …

Driven by the Quality of Experience (QoE) demands for video analysis applications within
contexts such as smart cities, Industrial Internet of Things (IoT), and Internet of Vehicles …

Video analytics demand intensive computation resources, which means long processing
delay when running on mobile devices. Although offloading computation to the cloud can …

Edge computing has emerged as a powerful technology for enhancing the performance of
on-device video analytics, which is critical to support real-time applications. Nevertheless …

In Mobile Edge Computing (MEC), the collaboration between end devices and servers
guarantees the low-latency and high-accuracy video stream analysis. However, such …

In this paper, we propose an adaptive video streaming method which is inspired by deep
reinforcement learning in mobile edge computing systems for autonomous driving …

The rapid progress of deep learning-based techniques such as Convolutional Neural
Network (CNN) has enabled many emerging applications related to video analytics and …