CAME: Cloud-Assisted Motion Estimation for Mobile Video Compression and Transmission. Yuan Zhao, Lei Zhang, Xiaoqiang Ma Jiangchuan Liu, Hongbo Jiang

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1 CAME: Cloud-Assisted Motion Estimation for Mobile Video Compression and Transmission Yuan Zhao, Lei Zhang, Xiaoqiang Ma Jiangchuan Liu, Hongbo Jiang

2 Outline Background Challenges Solutions Our Approach Evaluation Conclusions

3 Background Video streaming contributes a dominant frac2on of the internet traffic Mobile devices as important end devices for internet video apps YouTube mobile: 400M views a day, 13% of total Mobile device users can watch or shot high- quality videos anywhere any2me

4 Challenges Limited energy on mobile devices A challenging task to compress high- quality videos Expensive bandwidth cost Unaffordable to transmit raw video from both energy and bandwidth perspec2ve

5 Solutions Do nothing, upload raw video: high bandwidth cost and high transmission energy consump2on Conduct video compression before uploading: high computa2on cost, or high energy consump2on Conduct simpler video compression before uploading: not effec2ve enough Transfer video to PC, compress and upload: not convenient, discourage user to share video anywhere any2me.

6 CAME Approach Leverage cloud server resources for mo2on es2ma2on, which is known to be the most computa2on- intensive step. No need to upload the whole video stream to cloud server. Derived from mesh- based mo2on es2ma2on, which is known to be highly effec2ve. Mobile devices only selects and uploads the key informa2on

7 CAME: A Big Picture Anchor Frame Cloud Server Predictive Frames Motion Vectors Cloud Server Compressed Video

8 CAME: System and Algorithms 1. On mobile device, raw video is divided to macro blocks(mbs). A reference frame and a mesh for each successive P- frame are extracted. 2. Mobile device uploads the reference frame and the meshes. 3. Cloud server conducts mesh- based mo2on es2ma2on, generated mo2on vectors(mvs) are pushed back to mobile devices. 4. Mobile device finishes video compression and uploads the compressed video.

9 Standard Mesh-based Motion Estimation Standard mesh mo2on es2ma2on Mesh nodes selected on reference frame Mo2on vectors calculated from the single mesh on each successive P- frame Mesh structures: Regular: triangular or rectangular Object- based Hierarchy structure

10 Standard Mesh-based Motion Estimation

11 Reversed Mesh-based Motion Estimation Reversed mesh- based mo2on es2ma2on Mesh nodes selected on each successive P- frame Mo2on vectors calculated from reference frame and mesh Regular triangular mesh structure Reference frame and mesh nodes uploaded Energy and transmission cost reduced Mesh mo2on es2ma2on performed on the cloud server

12 Mobile Device Algorithm

13 Cloud Server Algorithm

14 Cost analysis Total transmission cost for mesh uploading: where m is the number of MBs, C R is the cost of reference frame, and n is the number of P- frames in a single MB.

15 Cost analysis Cost of sub- Block mo2on es2ma2on where n is number of P- frame in a single MB, m is sub- block number inside a single P- frame.

16 Total Cost C = C m + C d + C f + C c + C o + C u where C m is the total cost for uploading all MBs C d is MV downloading cost C f is the total cost for sub block mo2on es2ma2on C c is the mo2on compensa2on cost C o is all other costs for video compression on the mobile device a^er mo2on es2ma2on C u is the final step mobile video uploading cost

17 Evaluation Total cost is evaluated as equivalent CPU cycles Video compression computa2on cost is recorded directly as CPU cycles Transmission cost is converted to equivalent CPU cycles based on [balasubranmanian, SIGCOMM 2009] and [yuan, TOCS 2006] Total cost is evaluated on a simulated system More generic, tes2ng result not specific to individual mobile devices

18 Total Energy Consumption

19 Total Transmission

20 Conclusions The proposed approach can significantly reduce the complexity of video compression on mobile devices Beeer balance between energy and bandwidth consump2on One limita2on is the closed loop design of the system which may introduce unexpected delays.

21 Thanks Q & A

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