We propose a novel network coding (NC) enabled named data networking (NDN) architecture for scalable video delivery. Our architecture utilizes NC in order to address the problem that arises in the original NDN architecture, where optimal use of the bandwidth and caching resources necessitates the coordination of the Interest forwarding decisions. To optimize the performance of the proposed NC-based NDN architecture and render it appropriate for transmission of scalable video, we devise a novel rate allocation algorithm that decides on the optimal rates of Interests sent by clients and intermediate nodes. The flow of Data packets achieved by this algorithm maximizes the average quality of the video delivered to the client population. To support the handling of Interest and Data packets when intermediate nodes perform NC, we introduce the use of Bloom filters, which store efficiently additional information about the Interest and Data packets, and modify accordingly the standard NDN architecture. We also devise an optimized Interest forwarding strategy that implements the target rate allocation. The proposed architecture is evaluated for transmission of scalable video over PlanetLab topologies. The evaluation shows that the proposed scheme exploits optimally the available network resources.

The fast and huge increase of Internet traffic motivates the development of new communication methods that can deal with the growing volume of data traffic. To this aim, named data networking (NDN) has been proposed as a future Internet architecture that enables ubiquitous in-network caching and naturally supports multipath data delivery. Particular attention has been given to using dynamic adaptive streaming over HTTP to enable video streaming in NDN as in both schemes data transmission is triggered and controlled by the clients. However, state-of-the-art works do not consider the multipath capabilities of NDN and the potential improvements that multipath communication brings, such as increased throughput and reliability, which are fundamental for video streaming systems. In this paper, we present a novel architecture for dynamic adaptive streaming over network coding enabled NDN. In comparison to previous works proposing dynamic adaptive streaming over NDN, our architecture exploits network coding to efficiently use the multiple paths connecting the clients to the sources. Moreover, our architecture enables efficient multisource video streaming and improves resiliency to Data packet losses. The experimental evaluation shows that our architecture leads to reduced data traffic load on the sources, increased cache-hit rate at the in-network caches and faster adaptation of the requested video quality by the clients. The performance gains are verified through simulations in a Netflix-like scenario.

Content-Centric Networking (CCN) naturally supports multi-path communication, as it allows the simultaneous use of multiple interfaces (e.g. LTE and WiFi). When multiple sources and multiple clients are considered, the optimal set of distribution trees should be determined in order to optimally use all the available interfaces. This is not a trivial task, as it is a computationally intense procedure that should be done centrally. The need for central coordination can be removed by employing network coding, which also offers improved resiliency to errors and large throughput gains. In this paper, we propose NetCodCCN, a protocol for integrating network coding in CCN. In comparison to previous works proposing to enable network coding in CCN, NetCodCCN permits Interest aggregation and Interest pipelining, which reduce the data retrieval times. The experimental evaluation shows that the proposed protocol leads to significant improvements in terms of content retrieval delay compared to the original CCN. Our results demonstrate that the use of network coding adds robustness to losses and permits to exploit more efficiently the available network resources. The performance gains are verified for content retrieval in various network scenarios.

We consider the benefits brought by network coding in Information-Centric Networks (ICNs) in the case of a video streaming application. Network coding, when combined with ICN, allows a data transfer session to use multiple sources for the content seamlessly. It permits the client fetching the content to use multiple interfaces at the same time in an asynchronous manner, while using their capacity in an additive manner. This allows to create a logical link between the user and the content. In the case of video streaming, this logical link allows the rate adaptation logic to find the proper streaming rate while using multiple links concurrently. We implemented a video streaming system which works using network coding and CCN. We have shown that this implementation performs satisfactorily, and has comparable performance to a system without network coding in the unicast single source, single path case, and delivers significant performance gain (better QoE, higher throughput) when the video client retrieves the stream from multiple concurrent sources. We hope to demonstrate that network coding and CCN provides seamless mobility for a video streaming application.

This paper explores the potential of fusing social and sensor data in the cloud, presenting a practice - a travel recommendation system that offers the predicted mood information of people on where and when users wish to travel. The system is built upon a conceptual framework that allows to blend the heterogeneous social and sensor data for integrated analysis, extracting weather-dependant people's mood information from Twitter and meteorological sensor data streams. In order to handle massively streaming data, the system employs various cloud-serving systems, such as Hadoop, HBase, and GSN. Using this scalable system, we performed heavy ETL as well as filtering jobs, resulting in 12 million tweets over four months. We then derived a rich set of interesting findings through the data fusion, proving that our approach is effective and scalable, which can serve as an important basis in fusing social and sensor data in the cloud.

We present a novel receiver-driven p2p system for delivery of multiple concurrent time constrained data streams in overlay networks. We propose an effective combination of rateless coding with intra- and inter-session network coding to efficiently exploit the path diversity in the streaming overlay. Network nodes can decide to forward rateless coded packets or to code them in intra or inter-session mode before transmission. The transmission strategy is determined based on the availability of data sources and the demands of the children nodes. Each network node solves independently a simple flow maximization problem in order to determine the optimal coding policy. The overall system is evaluated for various networks and the results outline the advantages of the proposed approach over intra-session network coding based schemes in terms of clients' satisfaction, innovative flow rate and decoding delay.