Video streaming is crucial for AI applications that gather videos from sources to servers for
inference by deep neural nets (DNNs). Unlike traditional video streaming that optimizes …

Advances in deep learning (DL) have prompted the development of cloud-hosted DL-based
media applications that process video and audio streams in real-time. Such applications …

The advent of high-accuracy and resource-intensive deep neural networks (DNNs) has
fulled the development of live video analytics, where camera videos need to be streamed …

Live video accounts for a significant volume of today's Internet video. Despite a large
number of efforts to enhance user quality of experience (QoE) both at the ingest and …

Layered video coding compresses video segments into layers (additional code bits).
Decoding with each additional layer improves video quality incrementally. This approach …

High-quality computer vision models typically address the problem of understanding the
general distribution of real-world images. However, most cameras observe only a very small …

Continuous learning has recently shown promising results for video analytics by adapting a
lightweight" expert" DNN model for each specific video scene to cope with the data drift in …

Video cameras have been deployed at scale today. Driven by the breakthrough in deep
learning (DL), organizations that have deployed these cameras start to use DL-based …

Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality
of experience (QoE). Despite the abundance of recently proposed schemes, state-of-the-art …

Convolutional neural network inference on video data requires powerful hardware for real-
time processing. Given the inherent coherence across consecutive frames, large parts of a …