An autonomic delivery framework for HTTP adaptive streaming in multicast-enabled multimedia access networks

The consumption of multimedia services over HTTP-based delivery mechanisms has recently gained popularity due to their increased flexibility and reliability. Traditional broadcast TV channels are now offered over the Internet, in order to support Live TV for a broad range of consumer devices. Moreover, service providers can greatly benefit from offering external live content (e. g., YouTube, Hulu) in a managed way. Recently, HTTP Adaptive Streaming (HAS) techniques have been proposed in which video clients dynamically adapt their requested video quality level based on the current network and device state. Unlike linear TV, traditional HTTP- and HAS-based video streaming services depend on unicast sessions, leading to a network traffic load proportional to the number of multimedia consumers. In this paper we propose a novel HAS-based video delivery architecture, which features intelligent multicasting and caching in order to decrease the required bandwidth considerably in a Live TV scenario. Furthermore we discuss the autonomic selection of multicasted content to support Video on Demand (VoD) sessions. Experiments were conducted on a large scale and realistic emulation environment and compared with a traditional HAS-based media delivery setup using only unicast connections

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

MENU: multicast emulation using netlets and unicast

High-end networking applications such as Internet TV and software distribution have generated a demand for multicast protocols as an integral part of the network. This will allow such applications to support data dissemination to large groups of users in a scalable and reliable manner. Existing IP multicast protocols lack these features and also require state storage in the core of the network which is costly to implement. In this paper, we present a new multicast protocol referred to as MENU. It realises a scalable and a reliable multicast protocol model by pushing the tree building complexity to the edges of the network, thereby eliminating processing and state storage in the core of the network. The MENU protocol builds multicast support in the network using mobile agent based active network services, Netlets, and unicast addresses. The multicast delivery tree in MENU is a two level hierarchical structure where users are partitioned into client communities based on geographical proximity. Each client community in the network is treated as a single virtual destination for traffic from the server. Netlet based services referred to as hot spot delegates (HSDs) are deployed by servers at "hot spots" close to each client community. They function as virtual traffic destinations for the traffic from the server and also act as virtual source nodes for all users in the community. The source node feeds data to these distributed HSDs which in turn forward data to all downstream users through a locally constructed traffic delivery tree. It is shown through simulations that the resulting system provides an efficient means to incrementally build a source customisable secured multicast protocol which is both scalable and reliable. Furthermore, results show that MENU employs minimal processing and reduced state information in networks when compared to existing IP multicast protocols

GRIDKIT: Pluggable overlay networks for Grid computing

A `second generation' approach to the provision of Grid middleware is now emerging which is built on service-oriented architecture and web services standards and technologies. However, advanced Grid applications have significant demands that are not addressed by present-day web services platforms. As one prime example, current platforms do not support the rich diversity of communication `interaction types' that are demanded by advanced applications (e.g. publish-subscribe, media streaming, peer-to-peer interaction). In the paper we describe the Gridkit middleware which augments the basic service-oriented architecture to address this particular deficiency. We particularly focus on the communications infrastructure support required to support multiple interaction types in a unified, principled and extensible manner-which we present in terms of the novel concept of pluggable overlay networks

Scalable and Reliable File Transfer for Clusters Using Multicast.

A cluster is a group of computing resources that are connected by a single computer network and are managed as a single system. Clusters potentially have three key advantages over workstations operated in isolation—fault tolerance, load balancing and support for distributed computing. Information sharing among the cluster’s resources affects all phases of cluster administration. The thesis describes a new tool for distributing files within clusters. This tool, the Scalable and Reliable File Transfer Tool (SRFTT), uses Forward Error Correction (FEC) and multiple multicast channels to achieve an efficient reliable file transfer, relative to heterogeneous clusters. SRFTT achieves scalability by avoiding feedback from the receivers. Tests show that, for large files, retransmitting recovery information on multiple multicast channels gives significant performance gains when compared to a single retransmission channel