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In building video communications systems for groups we believe that the way we choose to transmit the signals over networks, will have a significant impact on the overall efficacy of the group communications. Ad hoc video communication between groups, in which the scene at each end is captured with multiple cameras, and in which each end point receives a different view of the emerging communication develops significant amount of network traffic.  At one extreme we could send every signal from every camera to every end point – but this ‘full mesh’ network soon becomes problematic.

From the point of view of an end user they may find that their link does not have enough upstream bandwidth to deliver signals from all of their cameras to all the other end points. From a network provider’s point of view, such a full mesh system is clearly inefficient as many versions of each video signal are flowing over a single stretch of network.

Of course this is not a new problem; video conferencing service providers have developed the idea of an MCUs (a multi-point control unit) that aggregates the input signals from each end point and then replicate the streams to send them on to all the other participants. MCUs are dedicated expensive piece of network equipment. In our work we explore the way network function virtualisation ideas can be used to recreate the capability of an MCU on standard industry computing hardware so that this capability can be enabled on an as needs basis on a cloud computing infrastructure.

The likely future growth of web-based video chat-room services (using technologies such as HTML5 and WebRTC) could make the need to rapidly and dynamically reconfigure network components an attractive option to operators seeking to reduce costs while maintaining customer experience. The scalability of the Service-Aware Network is a difficult challenge to address practically in a research project, so Vconect has turned to simulation and data analysis in order to bridge its work on network optimisation for user trials with the conditions anticipated in a large scale deployment of similar services.

Understanding the complex interplay between network characteristics such as protocol choice, bandwidth availability, delay, jitter, packet loss and their respective and combined effect on the quality of experience is not understood. One goal of this work is to better understand how network performance affects the QoE for groups video communications.