Robust Multimedia Streaming Services

The next generation of streaming services will be the basis of personalized applications, where everybody will be able to receive media content tailored to his/her own preferences, character, and context. We expect a high degree of interactivity in these applications. We see that participation of viewers is desirable for customers: reality shows on TV, Internet games and Blogs are indicators. The popularity of these applications is clear; they provide a highly personal experience. We expect that this trend will continue and expand, and we will have to account for more applications where the clear separation between producers of content and their customers will no longer exist. It should be possible for anyone to potentially play the content provider role. We will therefore rather define two roles that an actor in a next generation application can play: the producer and the consumer. An actor might even play both roles within the same application at the same time - in P2P file sharing this is the case today. Personalization already exists both on the Internet and with mobile technologies. There, operators learn about their customers, draw their profile and push them content. In our scenario, producers deliver their services across the networks of several operators. For consumers who use an interactive application, they have to deliver their service independently of network technology and end-device. Consumers may use devices that are connected to multiple networks at the same time or devices that require hand-over between networks. Heterogeneity of networks and terminals is one of the sources of complexity in the above scenario. A challenge is to provide each consumer with the best possible experience when considering resource limitations, context and individual preferences, while maintaining efficiency and scalability in all systems that are involved in the delivery from producer to consumer. This becomes even more complicated when actors change recording or display devices while an interactive streaming session is in progress. Context changes in this proposal are limited to include changes in computational resource availability (CPU, memory, network) and the joining and leaving of devices. The sources for heterogeneity are manifold. Actors are connected to networks by diverse technologies which have rather different characteristics. Similarly, terminal capabilities differ radically with respect to processing capabilities, display capabilities, and power availability. Different consumers have different and even conflicting preferences regarding personalization, and a single consumer may even have different preferences depending on context or on the terminal he is using at a given time. Streaming systems also have to cope with variability on a shorter time scale. The streamed content itself changes over time, which often translates into variable resource requirements, for example, with respect to bit rates and processing needs. The resource availability may also change over time. Handling heterogeneity and variability is further complicated by the need for efficient one-to-many or even many-to-many delivery. A challenge here is how to provide each consumer with fine granularity personalization opportunities while avoiding that the streaming servers have to handle each subscriber individually. The overall challenge resulting from the discussion above is how the provision of many-to-many multimedia streaming services can be made robust with respect to delivery to an open-ended range of devices operating in highly heterogeneous and possibly dynamically varying networking environments, and where the distribution system is partially shared and without centralized control, and where consumers have individual context-dependent preferences with respect to the quality and modality of the streaming media.