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View-Casting: View-based Stream Dissemination and Control for Multi- Party 3D Tele-immersive Environments Klara Nahrstedt University of Illinois at Urbana-Champaign.

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Presentation on theme: "View-Casting: View-based Stream Dissemination and Control for Multi- Party 3D Tele-immersive Environments Klara Nahrstedt University of Illinois at Urbana-Champaign."— Presentation transcript:

1 View-Casting: View-based Stream Dissemination and Control for Multi- Party 3D Tele-immersive Environments Klara Nahrstedt University of Illinois at Urbana-Champaign Joint work with UC Berkeley: Prof. Ruzena Bajcsy, Dr. Gregorij Kurillov, Dr. Sang-Jung Hack (3D Video) Illinois: Dr. Zhenyu Yang, Dr. Bin Yu, Dr. Jin Liang, Dr. Yi Cui, Wanmin Wu (QoS Management), NAIST Japan: Dr. Morihiko Tamai, Prof. Keiichi Yasumoto (Wii Interfaces), UC Berkeley/Illinois: Lisa Wymore, Renata Sheppard (Dance Choreography)

2 Outline Motivation and Goals Challenges TEEVE Solution Details View Concept View Casting: view selection, forest-casting, BW allocation, delay-sensitive streaming Experimental Results Conclusion

3 Tele-immersive Environment site 1 site 2 site 3 Photo courtesy of Prof. Ruzena Bajcsy.

4 Tele-Immersive Environment (TI) Abstraction

5 TI Application Model 3D capturing 3D camera transmission 1 5 7 3 8 4 6 2 3D rendering user view 1 5 7 3 8 4 6 2

6 3D Camera Characteristics and their Challenges for Internet and Multimedia Systems 3D Camera RGB and Depth yield 5 Bytes per pixel Frame Size: 640x480 pixels Real-Time Capturing and Content Creation (Currently 10-15 fps, Desired 30-60 fps) 3D Camera needs one PC Need FireWire Bus for Cameras to get high throughput 3D Multi-camera Environment 10-20 3D Cameras per Room Camera Spacing Issue Different spacing for physical activity than collaborative conferencing 180 or 300 or 360 degree camera coverage

7 Data Model 3D frame for camera i at time t: f i,t Each pixel in the frame carries color+depth data and can be independently rendered Stream for camera i VS i = { f i,t1 f i,t2 … } Macro-frame F t = { f 1,t f 2,t … f n,t } TI stream TI site Camera Orientation O i … … 1n f 1,t1 f n,t1 F t1 … f 1,t2 f n,t2 F t2 VS 1 VS n TI UIUC F t1 F t2 F t3 VS 1 VS 2 VS 3

8 Implications: 4D Content Challenge Deal with Huge TI Frame Size F t Macro-frame (e.g., 10 3D Frames in one time instance) TI Frame rate Applications desire from 15-60 Macro-frames per second Huge bandwidth uncompressed One 3D stream S i : 640x480 (pixels/frame) x 5 (bytes/pixel) x15 (frames/sec) = 23,040,000 bytes/second (approx. 23MBytes/sec) = 184 Mbps One TI stream TI room (e.g., 10 3D Streams): 230,040,000 bytes/sec (approx. 230MBytes/sec = 1.84 Gpbs) If we increase frame-rate, explosion of bandwidth !!!!! Slow 3D Reconstruction, 3D Modeling, 3D Rendering 71ms per frame in 3D reconstruction, … Need 3D Compression Need Parallel Processing

9 D C G service gatewaydisplaycamera C C C G D D C C C G D G D D G D C G D UIUC UC Berkeley Multi-Party 3D Tele-Immersive System

10 Network Characteristics and their Challenges on Internet and Multimedia Systems Heterogeneous networks/mismatch in bandwidth (LAN/WAN) LANs with 100 Mbps Ethernet cards and Gigabit Ethernet switch WAN Internet2 network with a 100 Mbps bottleneck link between UIUC and UC Berkeley Varying Internet2 bandwidth depending on the time of the day TCP/IP Protocol Stack due to large packet sizes, hence smaller context switching overhead No Real-time/no QoS capabilities in Internet2 or OS running TCP/IP stack

11 Implications: Timing Challenges Internet WAN LAN 1 2 N2 N 1 Tsend(i) FiFi Need Multi-stream Coordination Synchronization of multiple stream Synchrony at frame capturing time Spacing at Fj’s sending time Jitter control at Fj’s receiving time Skew control at rendering/display time Deal with limited buffering at IP routers TCP/IP retransmission delays different end-to-end (LAN/WAN) delays

12 Implications: Distributed Operating System Challenges Deal with Large scale of cameras/displays  Camera/Display PCs are distributed over LAN and WAN networks  PCs are multi-core Real-Time Processing (3D Reconstruction, Rendering)  Consider between 5-15 3D streams with frame rate of 10 fps and render them into one 4D stream with 10 fps  Run rendering station at 50-150 fps with 640x480 pixels per frame Need Configuration/coordination of various 3D/4D services in a flexible and effective manner Software synchronization protocols with approximate hardware accuracy Portable and easy deployable distributed TI/OS services Low maintenance OS services Real-Time OS services on multi-core architectures

13 Implications: Networking Challenges Deal with Large Bandwidth Demand  One 3D stream: 23MBytes/sec  One TI (e.g., 10 3D Streams): 230MBytes/sec) Real-Time Communication for Interactivity  80-100 millisecond interactive delay Need Multi-stream Coordination  due to large bursts at routers, need packet spacing Smart Delay/Bandwidth Management over Best Effort Internet Synchronization of Multiple Streams  need to send all streams within a constrained time interval so that rendered frame can be done in timely fashion Dealing with Scale  multi-party / many-to-many multicast forest protocols (camera arrays, participants, microphone arrays, …)

14 TEEVE Architecture

15 Network Model D C 3D display3D camera C C C D D C C C D D D C D Internet D C C SG service gateway session controller

16 TEEVE Service Middleware Stream Selection Bandwidth Allocation/View-Casting Streaming Control Service Middleware Networking Tele-immersive Application

17 1 … N … f 1,tm f N,tm F tm 3D macro-frame Service Middleware Layer … f 1,t1 f N,t1 3D renderer Networking Layer (Internet2) … f 1,t2 f N,t2 F t2 … f 1,t1 f N,t1 F t1 … … f 1,t2 f N,t2 … f 1,tm f N,tm … 3D stream O1O1 ONON OuOu Data Input and Output

18 user view camera view network Streaming control frame rate feedback Bandwidth allocation View-Cast Bandwidth Allocation Streaming Control Renderer bandwidth estimation Stream Selection Stream selection bandwidth allocation scheme selected streams Semantic Multi-Camera Stream Management View-based Forest Dissemination and Control

19 Multi-Camera Stream Selection (1) Determine which streams are of interest given the current user view The color portion of a macro-frame (Photo courtesy of Sang-Hack Jung.)

20 Multi-Camera Stream Selection (2) View-based approach Free view selection is a key feature of 3D video User selects view Reasonable to use the user view as the major QoS control feedback Adaptation based on the contribution of each 3D stream to the current user view Metric: Contribution factor based on the angle between the camera and the user view

21 View-aware Stream Differentiation (3) 3D capturing 1 5 7 3 8 4 6 2 3D camera transmission 1 5 7 3 8 4 6 2 3D rendering user view streams contributing more to user view less important streams

22 user viewing direction camera direction source: Angle θ Stream Selection (4)

23 user viewing direction camera direction source: Angle θ View Model s.fov v.w

24 View-aware Stream Differentiation Differentiation function df(s, v) = s.fov · v.w (dot product of unit vectors) When s.fov and v.w are unit vectors, the dot product gives cos  where  is the angle between s.fov and v.w Therefore, if s.fov and v.w are close, df(s, v) is close to 1. Otherwise, df(s, v) goes to -1. Select streams with df > T (T being threshold) of – optimization function for selected view across multiple parties of(S’,v) =  s  S’ df(s,v)

25 Differentiating Factor Metric (df) 3D capturing 1 5 7 3 8 4 6 2 3D camera transmission 1 5 7 3 8 4 6 2 3D rendering User/node’s view request v.w streams contributing more to user view s.fov for S4 s.fov for S5 less important streams df(S4,v) = s.fov (S4)*v.w df(S5,v) = s.fov(S5)*v.w of(S,v) = df(S4,v)+df(S5,v)

26 ViewCast (VC) – Problem Formulation Minimum Quality Problem Satisfy  v i  V  of(R i, v i )   Subject to system constraints NP-hard Maximum Quality Problem Maximize  i of(R i,v i ) Subject to system constraints NP-hard

27 VC – General Guideline Minimum quality guarantee: each renderer should receive a minimum set of streams if possible View change resilience: when user view is changed, the impact on other affected nodes should be minimized

28 V2V2 V3V3 V4V4 V1V1 U2.wU2.w U3.wU3.w U4.wU4.w S 4, S 5 S 2, S 3 U2U2 session controller U3U3 S4S4 U4U4 S 7, S 8 U3.wU3.w victim Why view change is a problem? Research: On Going

29 stream selection bandwidth allocation VS 1 fs A1A1 VS 2 VS i fs Ai'Ai' AmAm … VS j … … VS N   TFS quality Bandwidth Allocation (1)

30 Bandwidth Allocation (2) Frame size allocation TFS : target macro-frame size estimated by the underlying streaming control Given macro-frame F t and TFS, allocate the frame size (A i ) for each frame (f i,t ) Priority scheme based on CF

31 Bandwidth Allocation (3) Frame size allocation A i is allocated in descending order of CF fs: frame size of 3D frame If else

32 Streaming Control (1) Basic end-to-end protocol sender receives bandwidth allocation generates token = ((VS 1, A 1 ), (VS 2, A 2 ), …, (VS N, A N )) passes token along camera clusters receiver maintains periodic timer for rendering at fixed interval monitors the deadline of each macro-frame when the last frame of current macro-frame received at T  calculate the difference between T and deadline  send the difference back to sender

33 1 2 3 4 5 Network 1 2 3 4 5 deadline T macro-frame (after adaptation) streaming control streaming control e(t) = deadline  T bandwidth allocation token

34 Experimental Examples of Tele-immersive Environments TI in UIUC (Prof. Nahrstedt)TI in UC Berkeley (Prof. Bajcsy) Internet 2

35 Experimental Setup (TEEVE) Metrics: Overall throughput of macro-frame F j Completion time interval for macro-frame F j Individual throughput of stream i End-to-end Delay of a macro-frame F j Experimental parameters of remote testbed Number of sender gateways 2 Number of receiver gateways 2 Number of 3D streams 12 UIUC-UCB Equipment Dell precision 450 (Dual Xeon processor with 1 GBytes memory), running Fedora Core 2 LAN 100 Mbps and Internet 2 between UCB and UIUC

36 Timing Performance Validation Macro-Frame Delay (End-to-end Delay – UIUC/UCB) Macro-frame Completion Interval at the receiver side

37 Sender rate of each cluster camera – 5-8fps Continuity Test

38 average: 70.99 ms (frame rate: 14 Hz) Performance of 3D Reconstruction Resolution of each 3D frame is 320x240 pixels

39 Bandwidth Performance between UIUC/UCB on Internet2 during Dance Experiment average: 44.5 Mbps

40 Evaluation TFS reduces

41 Dancer’s Perceptual Evaluation

42 Experiment Setup Simulated 3DTI session topologymesh (connected) # of vertices (n)5, 6,…, 10 # of edges (CR) 25%, 50%, 75%, 100% (  n  (n-1)) degree bound (I i,O i )12, 24, 36 streams/vertex (|S i |)8 max. streams/view4 simulation time200 minutes view change interval normal distribution (  = 60 s,  = 10 s) view change pattern(a) random walk with average degree change of 30 , (b) Zipf (n = 10) delay bound (B)200 ms edge delay normal distribution (  = 80 ms,  = 20 ms)

43 Experiment Setup Counterpart for comparison, MC–n A multicast-based scheme where n streams (n =1, 2, 3, or 4) must be retrieved to serve a view. Otherwise, the view request is rejected. No preemption.

44 Rejection Ratio CR = 100%, I i (O i ) = 12 MC–4 MC–3 MC–2 MC–1 VC

45 Rejection Ratio CR = 100%, I i (O i ) = 24 MC–4 MC–3 MC–2 MC–1 VC

46 Streams/View CR = 100%, I i (O i ) = 24 average 3.2 better than MC–3 performance but with 22% less rejection ratio

47 Conclusion Question: Shall we dance …. across thousands of miles? Answer: We can waltz, but not tango yet!!!

48 References Z. Yang, K. Nahrstedt, Y. Cui, B. Yu, J. Liang, S-J. Hack, R. Bajcsy “ TEEVE: The Next Generation Architecture for Tele- immersive Environments”, IEEE International Symposium on Multimedia (ISM 2005), Irvine, CA, 2005 Z. Yang, B. Yu, K. Nahrstedt, R. Bajcsy “ A Multi-stream Adaptation Framework for Bandwidth Management in 3D Tele-Immersion”, ACM International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV 2006), New York, NY, 2006 M. Tamai, W. Wu, K. Nahrstedt, K. Yasumoto, “A View Control Interface for 3D Tele-Immersive Environments”, IEEE International Conference on Multimedia and Expo (ICME 2008), Hannover, Germany, 2008

49 Acknowledgement This work is being continuously supported by the National Science Foundation

50 Streaming Control (2) Bandwidth estimation Lots of schemes proposed (e.g., work by Dovrolis) Bandwidth estimation based on PI-controller (deadline  T ) is used as feedback e(t) in PI- controller to derive the suitable target frame size (i.e., TFS ).

51 Program Flow 1. Initializing data structures, PTG cameras, sockets 2. Establishing connections with trigger and gateway 3. Grabbing background images 4. Main grabbing and processing loop (new code using OpenMP) 4.1. Waiting for trigger 4.2 Grabbing images 4.3. Pre-process: median filter, background subtraction (new code using OpenCV functions, e.g., cvBacSub) 4.4 Computing depth (new code computing triangulation) 4.5 Building data packets and send to gateway

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