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Visual features constitute compact yet effective representations of visual content, and are being exploited in a large number of heterogeneous applications, including augmented reality, image registration, content-based retrieval, and classification. Several visual content analysis applications are distributed over a network and require the transmission of visual data, either in the pixel or in the feature domain, to a central unit that performs the task at hand. Furthermore, large-scale applications need to store a database composed of up to billions of features and perform matching with low latency. In this context, several different implementations of feature extraction algorithms have been proposed over the last few years, with the aim of reducing computational complexity and memory footprint, while maintaining an adequate level of accuracy. Besides extraction, a large body of research addressed the problem of ad-hoc feature encoding methods, and a number of networking and transmission protocols enabling distributed visual content analysis have been proposed. In this survey, we present an overview of state-of-the-art methods for the extraction, encoding, and transmission of compact features for visual content analysis, thoroughly addressing each step of the pipeline and highlighting the peculiarities of the proposed methods.