| Crowdsourced live video streaming from users is on the rise. Several factors such as social networks, streaming applications, smartphones with high-quality cameras, and ubiquitous wireless connectivity are contributing to this phenomenon. Unlike isolated professional videos, live streams emerge at an unprecedented scale, poorly captured, unorganized, and lack user context. To utilize the full potential of this medium and enable new services on top, immediate addressing of open challenges is required. Smartphones are resource constrained -- battery power is limited, bandwidth is scarce, on-board computing power and storage is insufficient to meet real-time demand. Therefore, mobile cloud computing is cited as an obvious alternative where cloud does the heavy-lifting for the smartphone. But, cloud resources are not cheap and real-time processing demands more than what the cloud can deliver.;This dissertation argues that throwing cloud resources at these problems and blindly offloading computation, while seemingly necessary, may not be sufficient. Opportunities need to be identified to streamline big-scale problems by leveraging in device capabilities, thereby making them amenable to a given cloud infrastructure. One of the key opportunities, we find, is the cross-correlation between different streams of information available in the cloud. We observe that inferences on a single information stream may often be difficult, but when viewed in conjunction with other information dimensions, the same problem often becomes tractable. |
