Advanced solutions for quality-oriented multimedia broadcasting

Multimedia content is increasingly being delivered via different types of networks to viewers in a variety of locations and contexts using a variety of devices. The ubiquitous nature of multimedia services comes at a cost, however. The successful delivery of multimedia services will require overcoming numerous technological challenges many of which have a direct effect on the quality of the multimedia experience. For example, due to dynamically changing requirements and networking conditions, the delivery of multimedia content has traditionally adopted a best effort approach. However, this approach has often led to the end-user perceived quality of multimedia-based services being negatively affected. Yet the quality of multimedia content is a vital issue for the continued acceptance and proliferation of these services. Indeed, end-users are becoming increasingly quality-aware in their expectations of multimedia experience and demand an ever-widening spectrum of rich multimedia-based services. As a consequence, there is a continuous and extensive research effort, by both industry and academia, to find solutions for improving the quality of multimedia content delivered to the users; as well, international standards bodies, such as the International Telecommunication Union (ITU), are renewing their effort on the standardization of multimedia technologies. There are very different directions in which research has attempted to find solutions in order to improve the quality of the rich media content delivered over various network types. It is in this context that this special issue on broadcast multimedia quality of the IEEE Transactions on Broadcasting illustrates some of these avenues and presents some of the most significant research results obtained by various teams of researchers from many countries. This special issue provides an example, albeit inevitably limited, of the richness and breath of the current research on multimedia broadcasting services. The research i- - ssues addressed in this special issue include, among others, factors that influence user perceived quality, encoding-related quality assessment and control, transmission and coverage-based solutions and objective quality measurements

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

SatCom Today in Canada: Significant Research: Broadband Satellite Communications List of CITR related Publications (1998-2003)

Journal Papers Conference Papers Contributions to Standards Canadian Space Agency Recent Publication

Network streaming and compression for mixed reality tele-immersion

Bulterman, D.C.A. [Promotor]Cesar, P.S. [Copromotor

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

Scalable Multiple Description Coding and Distributed Video Streaming over 3G Mobile Networks

In this thesis, a novel Scalable Multiple Description Coding (SMDC) framework is proposed. To address the bandwidth fluctuation, packet loss and heterogeneity problems in the wireless networks and further enhance the error resilience tools in Moving Pictures Experts Group 4 (MPEG-4), the joint design of layered coding (LC) and multiple description coding (MDC) is explored. It leverages a proposed distributed multimedia delivery mobile network (D-MDMN) to provide path diversity to combat streaming video outage due to handoff in Universal Mobile Telecommunications System (UMTS). The corresponding intra-RAN (Radio Access Network) handoff and inter-RAN handoff procedures in D-MDMN are studied in details, which employ the principle of video stream re-establishing to replace the principle of data forwarding in UMTS. Furthermore, a new IP (Internet Protocol) Differentiated Services (DiffServ) video marking algorithm is proposed to support the unequal error protection (UEP) of LC components of SMDC. Performance evaluation is carried through simulation using OPNET Modeler 9. 0. Simulation results show that the proposed handoff procedures in D-MDMN have better performance in terms of handoff latency, end-to-end delay and handoff scalability than that in UMTS. Performance evaluation of our proposed IP DiffServ video marking algorithm is also undertaken, which shows that it is more suitable for video streaming in IP mobile networks compared with the previously proposed DiffServ video marking algorithm (DVMA)

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise