Multimedia data dissemination in opportunistic systems

Opportunistic networks (OppNets) are human-centric mobile ad-hoc networks, in which neither the topology nor the participating nodes are known in advance. Routing is dynamically planned following the store-carry-and-forward paradigm, which takes advantage of people mobility. This widens the range of communication and supports indirect end-to-end data delivery. But due to individuals’ mobility, OppNets are characterized by frequent communication disruptions and uncertain data delivery. Hence, these networks are mostly used for exchanging small messages like disaster alarms or traffic notifications. Other scenarios that require the exchange of larger data (e.g. video) are still challenging due to the characteristics of this kind of networks. However, there are still multimedia sharing scenarios where a user might need switching from infrastructural communications to an ad-hoc alternative. Examples are the cases of 1) absence of infrastructural networks in far rural areas, 2) high costs due to roaming or limited data volumes or 3) undesirable censorship by third parties while exchanging sensitive content. Consequently, we target in this thesis a video dissemination scheme in OppNets. For the video delivery problem in the sparse opportunistic networks, we propose a solution with the objective of reducing the video playout delay, so that enabling the recipient to play the video content as soon as possible even if at a low quality. Furthermore, the received video reaches later a higher quality level, ensuring a better viewing experience. The proposed solution encloses three contributions. The first one is given by granulating the videos at the source node into smaller parts, and associating them with unequal redundancy degrees. This is technically based on using the Scalable Video Coding (SVC), which encodes a video into several layers of unequal importance for viewing the content at different quality levels. Layers are routed using the Sprayand-Wait routing protocol, with different redundancy factors for the different layers depending on their importance degree. In this context as well, a video viewing QoE metric is proposed, which takes the values of the perceived video quality, delivery delay and network overhead into consideration, and on a scalable basis. Second, we take advantage of the small units of the Network Abstraction Layer (NAL), which compose SVC layers. NAL units are packetized together under specific size constraints to optimize granularity. Packets sizes are tuned in an adaptive