  Mobile video applications today have attracted significant attention. Deep
learning model (e.g. deep neural network, DNN) compression is widely used to
enable on-device inference for facilitating robust and private mobile video
applications. The compressed DNN, however, is vulnerable to the agnostic data
drift of the live video captured from the dynamically changing mobile
scenarios. To combat the data drift, mobile ends rely on edge servers to
continuously evolve and re-compress the DNN with freshly collected data. We
design a framework, AdaEvo, that efficiently supports the resource-limited edge
server handling mobile DNN evolution tasks from multiple mobile ends. The key
goal of AdaEvo is to maximize the average quality of experience (QoE), e.g. the
proportion of high-quality DNN service time to the entire life cycle, for all
mobile ends. Specifically, it estimates the DNN accuracy drops at the mobile
end without labels and performs a dedicated video frame sampling strategy to
control the size of retraining data. In addition, it balances the limited
computing and memory resources on the edge server and the competition between
asynchronous tasks initiated by different mobile users. With an extensive
evaluation of real-world videos from mobile scenarios and across four diverse
mobile tasks, experimental results show that AdaEvo enables up to 34% accuracy
improvement and 32% average QoE improvement.
