To improve the visualization of large 3D landscapes and city models in a network environment, the authors use two different types of hierarchical level-of-detail models for terrain and groups of buildings. They also leverage the models to implement progressive streaming in both client-server and peer-to-peer network architectures.

The ever increasing speed of Internet connections has led to a point where it is actually possible for every end user to seamlessly share data on Internet. Peer-To-Peer (P2P) networks are typical of this evolution. The goal of our paper is to show that server-less P2P networks with self-adaptive assignment techniques can efficiently deal with very large environments such as met in the geovisualization domain. Our method allows adaptative view-dependent visualization thanks to a hierarchical and progressive data structure that describes the environment. In order to assess the global efficiency of this P2P technique, we have implemented a dedicated real time simulator. Experimentation results are presented using a hierarchical LOD model of a very large urban environment.

Peer-to-peer (P2P) architectures have recently become very popular in massively multiplayer games (MMGs). While P2P gaming offers high scalability compared to client/server architectures, it introduces several major issues related to data distribution and game state consistency. In this paper, we report our initial version of VoroGame, a P2P architecture for MMGs that addresses these issues by combining a structured P2P overlay based on a distributed hash table (DHT) for data distribution, with a Voronoi diagram used for virtual game world decomposition and semantic overlay support. The resulting hybrid architecture enables a fully distributed management of data and state information, and ensures efficient dissemination of game state updates to relevant peers.

International audienceAlthough compression of 3D content has been a widely studied topic, adaptive streaming of large virtual scenes is progressively becoming the standard way to share collaborative environments, due to the growing complexity of the shared data. The classical 'download and play' paradigm associated to standalone compression is leaving place to interactive refinements of regions-of-interest, provided given polygon budget and network capability. With the increasing number of applications and potential clients sharing reusable data, the traditional client / server architecture is likely to be over passed by less server-centered protocols, in order to distribute the complex tasks of identifying the selected data to be streamed. In this paper, we present the design and performances of a platform exploiting peer-to-peer transmission and show how the whole setting can be implemented using the MPEG-4 AFX standards

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On-demand video streaming applications are attracting an ever-growing number of users on the Internet. As new terminals, and especially mobile ones, give their users the ability to film and edit videos, it is necessary to provide efficient and scalable solutions that enable users to share their creations with as many users as they want. Traditional clientserver-based video streaming solutions can no longer cope with the ever-increasing number of users as they incur highly expensive bandwidth provision cost on the server. This paper addresses these issues for on-demand video streaming by proposing a dual spatially-organized peer-to-peer network. The idea is to distribute video streaming load by allowing real-time cache sharing between peers that are viewing approximately the same portion of the stream. Compared to existing P2P video streaming solutions, our solution (1) provides higher efficiency by connecting users who are watching almost the same part of the video, thus increasing the chance of finding missing video chunks at neighboring users, (2) reduces inter-ISP video traffic by privileging content exchange among peers within the same ISP network, and (3) provides incentives to share content by providing different service levels to users with different contributions to the system. Performance evaluations show the self-scalability of our solution in face of mass arrivals of users requesting the same video stream.

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