Making data the first class entity, Information-Centric Networking (ICN) replaces conventional host-to-host model with content sharing model. However, the huge amount of content and the volatility of replicas cached across the Internet pose significant challenges for addressing content only by name. In this paper, we propose a topology-aware name-based routing protocol which combines the benefits of location-oriented routing and content-centric routing together. We adopt a URL-like naming scheme, which defines register locations and content identifier. Node with copies sends Register messages towards a register using location-oriented routing protocols. All en-path routers record forwarding entries in forwarding table (FIB) as the "bread crumb" to this content. Following the bread crumb, routers know the "best" topology path to the available copies. An Interest is either forwarded towards a "known" copy by the content identifier, or towards the register nodes where it would find the bread crumb to the "best" copies. Compared with the existing flooding or name resolution methods, Our design shows a good potential in terms of scalability, availability and overhead.

Due to the complexity of video coding, fast transcoding is still a challenge. Various parallel coding methods have been proposed. In this paper, we present a parallel transcoding system over Map/Reduce cloud computing architecture. Input video sequences are divided into segments, and mapped to multiple computers. The sub-tasks are launched in parallel with processing results concatenated to the final output sequences. For heterogeneous clips, computing capacity, and task-launching overhead, the task scheduling over cloud is an NP-hard problem. We propose a low-complexity heuristic algorithm, Max-MCT, to find out the optimal solutions for task scheduling. By estimating the low-bound of finish time, we transform the problem into a virtual knapsack problem. But it is not an optimal solution for the original problem therefore we use a minimal complete time (MCT) algorithm to minimize the entire finish time. We carry out extensive experiments on numerical simulations. The results verified that our algorithm outperforms the existing algorithms.