This paper introduces the principal approach and describes the basic architecture and current implementation of the knowledge-based multimedia adaptation framework we are currently developing. The framework can be used in Universal Multimedia Access scenarios, where multimedia content has to be adapted to specific usage environment parameters (network and client device capabilities, user preferences). Using knowledge-based techniques (state-space planning), the framework automatically computes an adaptation plan, i.e., a sequence of media conversion operations, to transform the multimedia resources to meet the client's requirements or constraints. The system takes as input standards-compliant descriptions of the content (using MPEG-7 metadata) and of the target usage environment (using MPEG-21 Digital Item Adaptation metadata) to derive start and goal states for the planning process, respectively. Furthermore, declarative descriptions of the conversion operations (such as available via software library functions) enable existing adaptation algorithms to be invoked without requiring programming effort. A running example in the paper illustrates the descriptors and techniques employed by the knowledge-based media adaptation system.
Multimedia streaming is becoming more and more popular. Seamless video streaming in heterogeneous networks like the Internet turns out as almost impossible due to varying network conditions -- streams must be adapted to the current network QoS. Temporal scalability is one of the most reasonable adaptation techniques because it is fast and easy to perform. Today's approaches simply drop frames out of a video without spending much effort on finding an intelligent dropping behavior. This usually leads to good adaptation results in terms of bandwidth consumption but also to suboptimal video quality within the given bounds. Our approach offers analysis of video streams to achieve the qualitatively best temporal scalability. For this reason, we introduce a data structure called modification lattice which represents all frame dropping combinations within a sequence of frames. On the basis of the modification lattice, quality estimations on frame sequences can be performed. Moreover, a heuristic for fast and efficient quality computation in a modification lattice is presented. Experimental results illustrate that temporal video adaptation based on QCTVA information leads to a better video quality compared to "usual" frame dropping approaches. Furthermore, QCTVA offers frame priority lists for videos. Based on these priorities, numerous adaptation techniques can increase their overall performance when using QCTVA.