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FLoD-A Framework for Peer-to-Peer 3D Streaming. Outline. Introduction P2P Based 3D Streaming Requirements Challenges Framework Evaluation Conclusion. INTRODUCTION. 3D Objects. Objects are placed in a large scene Positions and orientations Associated data

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outline
Outline
  • Introduction
  • P2P Based 3D Streaming Requirements
  • Challenges
  • Framework
  • Evaluation
  • Conclusion
3d objects
3D Objects
  • Objects are placed in a large scene
  • Positions and orientations
  • Associated data
    • Polygonal meshes, Textures, Light maps, animations
  • Information is stored within a scene description
traditional 3d content
Traditional 3D Content
  • Require users to pre-installation or prior download
  • Undesirable - Web 3D
    • The future internet
    • If millions of 3D site were exist …
3d streaming features
3D Streaming Features
  • Continuous, real time delivery of 3D content
  • Allow user interactions without a prior download
  • 3D content needs to be fragmented into segment
  • Users accessing 3D content often have different visibility
  • Scalable and efficient 3D streaming may be important
3d streaming stages
3D Streaming Stages
  • Object determination
    • Employ visibility determination to pick object
    • Use visual quality estimates to assign transmission priority
  • Object transmission
    • Data reduction technique are used to send the object segment
p2p 3d streaming
P2P 3D Streaming
  • How can 3D streaming be realized for millions of concurrent users?
  • Users navigating may own similar content
  • 3D Data would resemble on-demand media streaming
  • The main different:
    • Content access pattern
    • Media streaming: linear (time)
    • 3D streaming: non-linear (area)
in this article
In This Article
  • FLoD: A Framework for Peer-to-Peer 3D Streaming
requirements
Requirements
  • 3D object data can be fragmented into
    • A base piece
    • many refinement pieces
  • scene can be rendered once the base pieces are obtained
requirements1
Requirements
  • Different fragmentation methods
    • Progressive meshes [7]
    • Geometry image [12]
    • Texture fragmentation [13]
  • Render and navigation may begin as soon as base pieces within the AOI art obtain
requirement for client
Requirement for Client
  • Main concern – Visual Quality
    • Visual perception [11]
    • How muchand how fasta client obtains data
    • Fill ratio
      • the ratio between the data currently owned and necessary to render a view at an instant
  • The goal is maximizing the fill ratio
measures for client
Measures for Client
  • Base latency
    • The time to obtain the base piece of an object
    • The delay for a user to see a basic view of an object
  • Completion latency
    • The time to download the complete data of an object
    • The delay of being able to fully inspect an object
requirement for server
Requirement for Server
  • Main concern - improve the system’s scalability
  • Content is delivered by Peer-to-Peer style
  • Minimize the server’s CPU and bandwidth usage
challenges
Distributed Visibility Determination

Dynamic group management

Peer and piece selection

CHALLENGES
distributed visibility determination
Distributed Visibility Determination
  • Visibility should be determined without the server or any global knowledge
  • We need to partition and distribute scene descriptions to all peers
dynamic group management
Dynamic group management
  • Peers exchange 3D data based on interest group
  • Involves the efficient discovery and maintenance of interest group
  • If users are moving constantly, the group is much more dynamic than media streaming
peer and piece selection
Peer and piece selection !!
  • Contact the proper peers
  • Request the proper data piece
  • Resource capacity, content availability, network
  • 3D streaming is view-dependent [14]
    • Data pieces may be applied in arbitrary order
    • Requires only a roughly sequential transfer order
conceptual model
Conceptual Model
  • Partition
    • Divide the entire scene into blocks
  • Fragmentation
    • Divide 3D objects into pieces
  • Prefetching
    • Predict data usage and generating object scene request
  • Prioritization
    • Perform visibility determination to generate the ordering of object pieces
  • Selection
    • Determining the proper peers to obtain pieces
    • Efficient fulfill request from prefetching and prioritization
flod framework
FLoD Framework
  • Graphics layer
    • Object determination

(prefetching, prioritization)

    • Object reconstruction

(de-partition, de-fragmentation)

  • Networking layer
    • Object transmission (peer and piece selection)
flod policies
FLoD Policies
  • Content Discovery
    • Query the neighbors first, then request the data from the neighbors
  • Peer Selection
    • Random or based on certain criteria. (peer’s bandwidth capacity)
  • Server Request Condition
    • To ask the server whenever other clients cannot respond to request
    • To ask the server only if the client becomes the nearest node to an object
scalability simulation
Scalability Simulation
  • The upload bandwidth for both C/S server and FLoD server
scalability simulation1
Scalability Simulation
  • The upload and download bandwidth of FLoD client
streaming quality
Streaming Quality
  • The fill ratio for both C/S and FLoD client
streaming quality1
Streaming Quality
  • FLoD client’s base latency is relatively stable at below 600ms
aoi neighbors
AOI Neighbors
  • If AOI neighbors do not exist
  • Server request ratio may decrease as nod densith
limitation
Limitation
  • Downgrade client’s upload to 64, 48, 32, 16, 8 KB/s
  • Look at the effect of data density
  • The fill ratio decreases as the client upload gets smaller
conclusion
Conclusion
  • This is a topic of interest to both graphics and networking.
  • Challenges:
    • Quality of client
    • Efficiency content delivery
    • Peer and piece selection
    • Cannot found AOI neighbors
references
References
  • [7] H. Hoppe, “Progressive meshes,” in Proc. SIGGRAPH, 1996.
  • [8] S.-Y. Hu, J.-F. Chen, and T.-H. Chen, “Von: A scalable peer-to-peernetwork for virtual environments,” IEEE Network, vol. 20, no. 4, pp.22–31, 2006.
  • [11] J. Chim, R. W. H. Lau, H. V. Leong, and A. Si, “Cyberwalk: A webbaseddistributed virtual walkthrough environment,” IEEE TMM, vol. 5,no. 4, pp. 503–515, 2003.
  • [12] N.-S. Lin, T.-H. Huang, and B.-Y. Chen, “3d model streaming based onjpeg 2000,” IEEE TCE, vol. 53, no. 1, 2007.
  • [13] J.-E. Marvie and K. Bouatouch, “Remote rendering of massively textured3d scenes through progressive texture maps,” in Proc. VIIP, 2003,pp. 756–761.
  • [14] J. Kim, S. Lee, and L. Kobbelt, “View-dependent streaming of progressivemeshes,” in Proc. SMI’04, 2004, pp. 209–220.