Optical Networks & Smart Grid Lab Nonlinear Auto-Regressive Neural Network Model for Forecasting Hi-Def H.265 Video Traffic Over Ethernet Passive Optical Networks Collin J. Daly, David L. Moore, Rami J. Haddad Department of Electrical Engineering Georgia Southern University
Outline Motivation Discussion of Dataset Proposed Prediction Models Simulation Parameters Discussion of Results Conclusion
Motivation Netflix, Hulu, YouTube 80% of all data will be video, 2 zettabytes 5 million years of video each month in 2019 Why Compress? 4K video contains 3840 x 2160 pixels per frame = 8,294,400 pixels 8 bits per pixel for color (low estimate) = 66,355,200 bits/frame 30 frames per second = 1,990,656,000 = 1.99Gb/s Ethernet IP networks are expanding, but not as quickly as utilization.
H.264 vs H.265 H.264 is current standard, adopted 2003 while H.265 adopted 2013. H.265 compresses video to a smaller size than what H.264 would. The same size file yields higher resolution in H.265. However, the higher compression ratio results in high variability in the video stream bitrate, which is detrimental to networks (Bandwidth Allocation).
EPON Basics Ethernet Passive Optical Network Fiber link from ISP to end users No active amplification Lower implementation costs Regulated network
EPON Architecture
Proposed Prediction Model Nonlinear Auto-Regressive (NAR) Neural Network
Dataset – Video Traces Arizona State University Video Trace Library P Arizona State University Video Trace Library Video traces vs regular video files All videos use a G24B7 group of pictures (GOP) format Quantization levels: 20, 25, 30, 35, 40, 45 Higher compression lowers quality
Video Traces Video Traces Utilized: Harry Potter Finding Neverland Speed Lake House
Finding Neverland Video Delay
Finding Neverland Video Delay
Finding Neverland Data Delay
Finding Neverland Data Delay
EPON Simulation Results
EPON Simulation Results
Conclusion Data delay increases while overall video delay decreases when predicted grants are issued Prediction is harmful at low quantization levels in an EPON environment Prediction is most beneficial when the network is nearing traffic saturation
Questions