Streaming of High-resolution Progressive Meshes Over The Internet

Abstract

High-resolution 3D meshes are increasingly available in networked applications, such as digital museum, online game, and virtual reality. The amount of data constituting a high-resolution 3D mesh can be huge, leading to long downloading time. To reduce the waiting time, a common technique for remote viewing is progressive streaming, which allows a low-resolution version of the mesh to be transmitted and rendered with low latency. The quality of the transmitted mesh is incrementally improved by continuously transmitting the refinement information.

Progressive mesh is commonly used to support progressive streaming. A progressive mesh comprises a base mesh and a series of refinements. The base mesh is obtained by simplifying the original mesh with a series of edge collapses. The inverses of these edge collapses, named vertex splits, can reconstruct the original mesh from the base mesh. Therefore, progressive streaming can be implemented by sending the vertex splits as refinements after sending the base mesh. Vertex splits of a progressive mesh can be sent in various orders, and this kind of flexibility leads to many new research problems. In this thesis, three such problems are addressed.

First, dependencies exist among the vertex splits, and the descendants cannot be decoded before their ancestors are all decoded. When a progressive mesh is transmitted over a lossy network, a packet loss will delay the decoding of any following vertex split that depends on this lost packet. Hence, the effect of dependency needs to be considered in choosing the sending order of vertex splits. In this thesis, an analytical model is proposed to quantify the effects of dependency by modeling the decoding time of each vertex split as a function of mesh properties and network parameters. Consequently, different sending orders can be efficiently evaluated, and this model can help in developing sending strategies to improve the quality during streaming. The accuracy of the analytical model is validated under a variety of network conditions, including bursty losses, fluctuating RTT, and varying sending rates. The values predicted from our model match the measured value reasonably well in all cases except when losses are too bursty.

Second, to improve the quality of rendered image on the receiver side quickly, the viewpoint of each user can be considered in deciding the sending order. In existing solutions to view-dependent streaming, the sender decides the sending order and maintains the rendering state of each receiver. Due to the stateful design, the sender-driven approach cannot be easily extended to support many receivers with common solutions to scalability. In this thesis, we proposed a receiver-driven protocol, in which the receiver decides the sending order and explicitly requests the vertex splits. The sending order is computed at the receiver by estimating the visibility and visual contributions of the refinements before receiving them. The sender in our receiver-driven protocol is stateless, so caching proxy and P2P streaming systems can be applied to improve the scalability without adding more servers.

Third, based on the receiver-driven protocol we proposed, P2P techniques are applied to view-dependent progressive mesh streaming in this thesis. Two issues are considered: how to partition a progressive mesh into chunks and how to lookup the provider of a chunk. We proposed a chunking method following the vertex hierarchy and investigated two solutions for the content discovery. The first one uses a simple centralized lookup service, while the second one organizes peers into groups according to the hierarchical structure of the progressive mesh to take advantage of the access pattern. We have implemented a prototype and simulation results show that our proposed systems reduces the server overhead by more than 90% and keeps low control overhead and response time.