We study Social Groups consisting of self-interested inter-connected nodes looking for common content. We can observe Social Groups in various socio-technological networks, such as Cellular Network assisted Device-to-Device communications, Cloud assisted Peer-to-Peer Networks, hybrid Peer-to-Peer Content Distribution Networks and Direct Connect Networks. Each node wants to acquire a universe of segments at least cost. Nodes can either access an expensive link to the content distributor for downloading data segments, or use the well-connected low cost inter-node network for exchanging segments among themselves. Activation of an inter-node link requires cooperation among the participating nodes and reduces the cost of downloading for the nodes. However, due to uploading costs, Non-Reciprocating Nodes are reluctant to upload segments, in spite of their interest in downloading segments from others. We define the Give-and-Take (GT) criterion, which prohibits non-reciprocating behaviour in Social Groups for all nodes at all instants. In the “Full Exchange” case studied, two nodes can exchange copies of their entire segment sets, if each node gains at least one new segment from the other. Incorporating the GT criterion in the Social Group, we study the problem of downloading the universe at least cost, from the perspective of a new node having no data segments. We analyze this NP-hard problem, and propose algorithms for choosing the initial segments to be downloaded from the content distributor and the sequence of nodes for exchange. We compare the performance of these algorithms with a few existing P2P downloading strategies in terms of cost and running time. In the second problem, we attempt to reduce the load on the content distributor by choosing a schedule of inter-node link activations such that the number of nodes with the universe is maximized. Link activation decisions are taken by a central entity, the facilitator, for achieving the social optimum. We present the asymptotically optimal Randomized algorithm. We also present other algorithms, such as the Greedy Links algorithm and the Polygon algorithm, which are optimal under special scenarios of interest. We compare the performances of all proposed algorithms with the optimal value of the objective. We observe that computationally intensive algorithms exhibit better performance. Further, we consider the problem of decentralized scheduling of links. The decisions of link activations are made by the participating nodes in a distributed manner. While conforming to the GT criterion for inter-node exchanges, each node's objective is to maximize its utility. Each node tries to find a pairing partner by preferentially exploring nodes for link formation. Unpaired nodes choose to download a segment using the expensive link with Segment Aggressiveness Probability (SAP). We present linear complexity decentralized algorithms for nodes to choose their best strategy. We present a decentralized randomized algorithm that works in the absence of the facilitator and performs close to optimal for large number of nodes. We define the Price of Choice to benchmark performance of Social Groups (consisting of non-aggressive nodes) with the optimal. We evaluate the performance of various algorithms and characterize the behavioural regime that will yield best results for node and Social Group as well.