Wide popularity of wireless devices and their data-enabled applications have created an evolving marketplace for digital content ecosystems. A common operation in those ecosystems is to disseminate content in a cost-optimal manner. With the conventional download model, a user downloads content directly from a Content Provider's (CP) server via a Communication Service Provider's (CSP) network. Downloading content through CSP's network involves a cost, which must be paid either by End Consumers (EC) or the CP. The main objective of the thesis is to provide caching mechanisms that minimizes the overall provisioning cost in different network topologies. This is implemented by caching right objects in data-enabled mobile devices such as smartphones, smart pads, vehicles and novel edge devices. In this thesis, several number of existing caching strategies are studied. Then, an incentive based cooperative content caching framework is developed for both fully-connected Social Wireless Networks (SWNETs) and mobile wireless networks in which content demands are hierarchically heterogeneous. Furthermore, a D2D cooperative caching framework is proposed for streaming video with heterogeneous quality demands in SWNETs. This caching framework contains two main components: a value-based caching strategy in which the value of caching a streaming video segment is defined for given pricing and video sharing models, and an Adaptive Quality (AQ) provisioning algorithm that minimizes the overall video content provisioning cost within an SWNET. Additionally, a vehicular content caching mechanism is developed for disseminating navigational maps while minimizing cellular network bandwidth usage. The key concept is to collaboratively cache the dynamic components of navigational maps in roadside units (RSUs) and vehicles such that the majority of dissemination can be accomplished using V2V and V2I communication links. Moreover, a novel caching mechanism is proposed which is based on Connectionless Edge Cache Servers in vehicular networks. The goal is to intelligently cache content within the vehicles and the edge servers so that majority of the vehicle-requested content can be obtained from those caches, thus minimizing the amount of cellular network usage needed for fetching content from a central server. A notable feature of the cache servers in this work is that they do not have backhaul connectivity. This makes the connectionless servers to be relatively less expensive compared to the usual Roadside Service Units (RSUs), and potentially moveable in response to specific events that are expected to generate content in large volumes. Finally, a list of future work on this topic is compiled that includes: 1) developing machine learning models for predicting content demand and spatiotemporal localities of node movements, 2) developing mechanisms for edge cache server placement for performance optimization, and 3) analyzing the impacts of selfishness on the performance of caching.
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