This article focuses on the compressed representations of the pictures

Abstract—Multiple description (MD) coding is source coding in which several descriptions of the source are produced such that various reconstruction qualities are obtained from different subsets of the descriptions. Unlike multiresolution or layered source coding, there is no hierarchy of descriptions; thus, MD coding is suitable for packet erasure channels or networks without priority provisions. Generalizing work by Orchard, Wang, Vaishampayan, and Reibman, a transform-based approach is developed for producing descriptions of an-tuple source,. The descriptions are sets of transform coefficients, and the transform coefficients of different descriptions are correlated so that missing coefficients can be estimated. Several transform optimization results are presented for memoryless Gaussian sources, including a complete solution of the aP, aPcase with arbitrary weighting of the descriptions. The technique is effective only when independent components of the source have differing variances. Numerical studies show that this method performs well at low redundancies, as compared to uniform MD scalar quantization. Index Terms—Erasure channels, integer-to-integer transforms, packet networks, robust source coding.

Prior work has shown the value of changing application fidelity to adapt to varying resource levels in a mobile environment. Choosing the right fidelity requires us to predict its effect on resource consumption. In this paper, we describe a history-based mechanism for such predictions. Our approach generates predictors that are specialized to the hardware on which the application runs, and to the specific input data on which it operates. We are able to predict the CPU consumption of a complex graphics application to within 20% and the energy consumption of fetching and rendering web images to within 15%.

WebSplitter symbolizes the union of pervasive multi-device computing and collaborative multi-user computing. WebSplitter provides a unified XML framework that enables multi-device and multi-user Web browsing. WebSplitter splits a requested Web page and delivers the appropriate partial view of each page to each user, or more accurately to each user's set of devices. Multiple users can participate in the same browsing session, as in traditional conferencing groupware. Depending on the access privileges of the user to the different components of content on each page, WebSplitter generates a personalized partial view. WebSplitter further splits the partial view among the devices available to each user, e.g. laptop, wireless PDA, projection display, stereo speakers, orchestrating a composite presentation across the devices. A wireless PDA can browse while remotely controlling the multimedia capabilities of nearby devices. The architecture consists of an XML metadata policy file defining access privileges to XML tags on a Web page, a middleware proxy that splits XML Web content to create partial views, and a client-side component, e.g. applet, enabling user login and reception of pushed browsing data. Service discovery finds and registers proxies, browsing sessions, and device capabilities. We demonstrate the feasibility of splitting the different tags in an XML Web page to different end users' browsers, and of pushing updates from the browsing session to heterogeneous devices, including a laptop and a PDA.

Transcoding is becoming a preferred technique to tailor multimedia objects for delivery across variable network bandwidth and for storage and display on the destination device. This paper presents techniques to quantify the quality-versus-size tradeoff characteristics for transcoding JPEG images. We analyze the characteristics of images available in typical Web sites and explore how we can perform informed transcoding using the JPEG compression metric. We present the effects of this transcoding on the image storage size and image information quality. We also present ways of predicting the computational cost as well as potential space benefits achieved by the transcoding. These results are useful in any system that uses transcoding to reduce access latencies, increase effective storage space as well as reduce access costs.

Transcoding is a technique employed by network proxies to dynamically customize multimedia objects for prevailing network conditions and individual client characteristics. Transcoding can be performed along a number of different axes and the specific transcoding technique used depends on the type of multimedia object. Our goal in this paper is to understand the nature of typical Internet images and their transcoding characteristics. We focus our attention on transcodings intended to customize an image for file size savings. Our results allow the developers of a transcoding proxy server to choose the appropriate transcoding techniques for the important classes of Internet images. We analyze the characteristics of images available on the Web through a representative trace. We show that most GIF images accessed on the Internet are small

Mobile applications will increasingly depend upon multimedia information originating in the web and attempt to access this data over wireless networks that are more expensive and slower than typical wireline internet access from desktops. Transcoding is an important technique that can allow network proxy servers to offer differentiated service by customizing the delivered object size for the network bandwidth available on the "last hop" to the mobile client. We exploit technology that we had previously developed that characterized the quality versus size tradeoffs in transcoding JPEG images. This technology supplies more information for use in transcoding policy decision making. We evaluate the performance benefits of incrementally incorporating this information in a series of transcoding policies. The principal contribution of this work is the demonstration that it is possible to use informed transcoding techniques to balance the need for good quality of multimedia content while redu...

Mobile applications execute in an environment characterized by scarce and dynamically varying resources. We believe that applications have to adapt dynamically and transparently to the amount of resources available at runtime. To achieve this goal, we use the conventional extension of the clientserver model to a client-proxy-server model. The mobile devices execute the client, which provides the user interface and some part of the application logic. The proxy is a component of the application that executes in the wired network to support the client. As the user moves, the proxy may also move to remain on the communication path from the mobile device to a fixed correspondent host. Logically, the proxy hides the "mobile" client from the server, who thinks it communicates with a standard client (i.e., a client that executes on a powerful desktop directly connected to the wired network). The new contribution of our research lies in the division of labor between client and proxy. The applic...

We propose a method that can find the optimal tradeo# point between transcoding overhead (CP cost) and storage needed for the various pre-processed content variants (I/O cost). The method selectively pre-adapts a subset of content variants and leaves the generation of the residue to dynamic content adaptation with this pre-adapted subset as an input. We prove bounds regarding the optimality of the algorithm employed. The proposed model creates a collaborative environment across the components of client, proxy and server, based on which we study the distribution of adaptation complexity across these components. We use simulation to verify the projected benefits. The method has been successfully implemented in a trial PDF document content adaptation system.

Advances in computing and communication technologies have resulted in a wide variety of networked mobile devices that access data over the Internet. In this paper, we argue that servers by themselves may not be able to handle this diversity in client characteristics and intermediate proxies should be employed to handle the mismatch between the server-supplied data and the client capabilities. Since existing proxies are primarily designed to handle traditional wired hosts, such proxy architectures will need to be enhanced to handle mobile devices. We propose such an enhanced proxy architecture that is capable of handling the heterogeneity in client needs---specifically the variations in client bandwidth and display capabilities. Our architecture combines transcoding (which is used to match the fidelity of the requested object to client capabilities) and caching (which is used to reduce the latency for accessing popular objects). Our proxies can intelligently adapt to prevailing system conditions using learning techniques to intelligently decide whether to transcode locally or fetch an appropriate version from the server. Our experimental results indicate that such strategies produce significant improvements in the client response times. Further, we find that even simple learning techniques can lead to significant performance improvements.