Collaborative Edge-Cloud and Edge-Edge Video Analytics

According to YouTube statistics [1], more than 400 hours of content is uploaded to its platform every minute. At this rate, it is estimated that it would take more than 70 years of continuous watch time in order to view all content on YouTube, assuming no more content is uploaded. This raises great challenges when attempting to actively process and analyze video content. Real-time video processing is a critical element that brings forth numerous applications otherwise infeasible due to scalability constraints. Predictive models are commonly used, specifically Neural Networks (NNs), to accelerate processing time when analyzing real-time content. However, applying NNs is computationally expensive. Advanced hardware (e.g. graphics processing units or GPUs) and cloud infrastructure are usually utilized to meet the demand of processing applications. Nevertheless, recent work in the field of edge computing aims to develop systems that relieve the load on the cloud by delegating parts of the job to edge nodes. Such systems emphasize processing as much as possible within the edge node before delegating the load to the cloud in hopes of reducing the latency. In addition, processing content in the edge promotes the privacy and security of the data. One example is the work by Grulich et al. [2] where the edge node relieves some of the work load off the cloud by splitting, differentiating and compressing the NN used to analyze the content. [GRAPHICS] .