1 / 36

ParCast : Soft Video Delivery in MIMO-OFDM WLANs

ParCast : Soft Video Delivery in MIMO-OFDM WLANs. Xiaolin Liu Wenjun Hu Qifan Pu Feng Wu Yongguang Zhang. Microsoft Research Asia University of Science and Technology of China. Wireless trends. Wireless trends. Channel capability. Trend: MIMO-OFDM becoming primitives.

happy
Download Presentation

ParCast : Soft Video Delivery in MIMO-OFDM WLANs

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. ParCast: Soft Video Delivery in MIMO-OFDM WLANs Xiaolin Liu Wenjun Hu Qifan Pu Feng Wu Yongguang Zhang Microsoft Research Asia University of Science and Technology of China

  2. Wireless trends

  3. Wireless trends • Channel capability Trend: MIMO-OFDM becoming primitives

  4. Wireless trends • Channel capability • Application demand Abcdef… Trend: MIMO-OFDM becoming primitives Trend: Video traffic dominates Video over wireless to rule!

  5. Traditionally… Divide into blocks and Transform Quantize + entropy coding 10000100101 00111001000 11010011001 01001001110 …… Separate source and channel codes Packetize Modulate 100001001011000011 001110010000011100 10000100101 00111001000 + protection 110100110011101001 010010011100101110 11010011001 01001001110 ……

  6. Challenge for source coding Digital rates do not fall back gracefully • Cliff effects at some SNR • Glitches due to bit errors Ideally, want graceful degradation with the channel Video quality Video quality Successfully received packet index Channel quality

  7. Challenge for channel coding Efficiency vs complexity tradeoff • 100+ heterogeneous subchannels in practice • E.g., 52 OFDM subcarriers in 802.11n per 20MHz, 2-4 MIMO spatial paths per subcarrier • One code for all does not fully utilize capacity • Subchannel specific modulation impractical Channel quality Ideally, want one code to gracefully degrade on all subchannels Subchannel index Unicast over MIMO-OFDM resembles broadcast over narrowband SISO channels

  8. Our solution • A single joint source-channel code to work gracefully with SNR on all subchannels • Take a leaf from SoftCast’s book • Apply similar principles to MIMO-OFDM unicast

  9. Source characteristics First-frame 8x8 block DCT coefficient energy distribution Energy difference over several orders of magnitude

  10. Source coding goals Essential to protect the most important bands well First-frame 8x8 block DCT coefficient energy distribution Discard least important bands for compression

  11. Channel characteristics 3x3 MIMO-OFDM subchannel gains on 20MHz channel Gain difference over several orders of magnitude

  12. Channel coding goals 3x3 MIMO-OFDM subchannel gains on 20MHz channel Want to utilize the good subchannels Want to mitigate effects from the bad subchannels

  13. Source-channel similarities Source: First-frame 8x8 block DCT coefficient energy distribution Channel: 3x3 MIMO-OFDM subchannel gains on 20MHz channel

  14. Source-channel synergy Source Decompose Allocate bits

  15. Source-channel synergy Joint Source Channel Power Freq Decompose Allocate power

  16. ParCast: Parallel Video Unicast

  17. ParCast: Parallel video unicast Preprocessing Sorting and matching Power allocation Freq Power Analog modulation and transmission b d f a c e a + bi c + di e + fi Decoding per subchannel Separating components

  18. ParCast: Preprocessing • Video source: • Channel: Whole-frame 3D-DCT Divide into chunks Decorrelate and separate source/channel components Real values with variances λ1 , λ2, … Precode s1 0 a+bi c+di s1 s2 f+gi h+ki 0 s2

  19. ParCast: Sorting and matching • Video source: chunks sorted by importance • Channel: subchannels sorted by gain • Matching Fine-grained unequal error protection

  20. ParCast: Power allocation • More power → higher rate/lower distortion • Can formulate/solve optimization problem • Fixed total budget • Minimize ∑ distortion over components • Source/channel component pair as unit • Power weight = f(λi/si2) Helpful when given similar energy spread for both source/channel

  21. ParCast: Power allocation • More power → higher rate/lower distortion • Can formulate/solve optimization problem • Fixed total budget • Minimize ∑ distortion over components • Source/channel component pair as unit • Power weight = f(λi/si2) • Discard pair if λi/si2 too small • Per-chunk whitening for hardware consideration Compression and protection in a single code Further unequal error protection

  22. ParCast: Analog modulation Chunk i: 1 2 3 4 5 6 7 8 9 I Complex subchannel Chunk j: 1 2 3 4 5 6 7 8 9 Q … Nspatial path x Nsubcarrier complex symbols … Preamble OFDM symbol OFDM symbol OFDM symbol Soft delivery per subchannel to leverage channel capacity 44 OFDM symbols per packet

  23. ParCast: Decoding • No standard MIMO decoding • LLSE to decode DCT coefficients per chunk • Inverse 3D-DCT

  24. ParCast: Parallel video unicast Preprocessing Sorting and matching Power allocation Freq Power Analog modulation and transmission b d f a c e a + bi c + di e + fi Decoding per subchannel Separating components

  25. ParCast: Parallel video unicast Freq Power b d f a c e a + bi c + di e + fi Parallel, independent encoding/transmission and reception/decoding

  26. ParCast: Parallel video unicast Freq Power b d f a c e a + bi c + di e + fi Joint source-channel coding Lossless compression over lossy communication

  27. ParCast: Parallel video unicast Freq Power Linear codec per subchannel for unicast over MIMO-OFDM b d f a c e a + bi c + di e + fi

  28. Performance

  29. Implementation • Source codec in Matlab • Channel dependent modules implemented on Sora • Channel trace driven simulation [Halperin et al, SIGCOMM 2010]

  30. Evaluation • Microbenchmarks • Software radio based experiments • Effects of individual modules • Channel precoding, matching, joint power allocation • Video quality (PSNR) Comparison • Variants of SoftCast adapted to MIMO • Omni-MPEG over 802.11n • Layered SVC over MIMO

  31. Performance: ParCastvsSoftCast • Must decorrelate both! • For best unequal protection • SoftCast does not work well over MIMO • Mixing source chunks = mixing subchannels Separate source, mixed channel SoftCast (mixed source) ParCast (Separate source/channel) Mixed source, separate channel

  32. Performance: Stationary links ~ 10 dB

  33. Performance: Mobile scenario football ~ 5 dB Fairly stable with delayed CSI Channel feedback delay period

  34. Related work • Unequal error protection over MIMO/OFDM • Layered video coding, one layer per subchannel • Channel codes for graceful degradation • HM over STBC (broadcast), Apex (unicast), etc. • Joint source-channel coding (single antenna) • Jointly optimizing separate codes • A single code for compression/protection • SoftCast (broadcast), FlexCast (unicast)

  35. Conclusion ParCastis simple yet effective • One code to work gracefully with SNR on all MIMO-OFDM subchannels • Joint source-channel coding • Motivated by source-channel synergy • Non-uniform distribution at both provides inherent unequal error protection • Parallel delivery per source chunk/subchannel • Treating unicast as broadcast • Graceful degradation via linear codec

  36. Thank you! Questions?

More Related