Optimizing the quality of scalable video streams on p2p networks Raj Kumar Rajendran Dan Rubenstein DNA Group, Columbia University

DNA Lab, Columbia University Motivation Multimedia Streams on P2P networks are growing in popularity P2P streams account for large fraction of TCP traffic! Our Goal: support live streaming. However bandwidth Varies widely with time Is often insufficient for high-quality video

DNA Lab, Columbia University Known Solutions Use buffering (pre-fetching) Divide stream into layers Facilitates multiple-peer downloads

DNA Lab, Columbia University Using Layered Coding Use Fine-Grained Scalable coding Required small base-layer Optional large enhancement layers Divide stream into M equally sized layers Time t Bitrate Base Layer Enhancement Layers Video bitrate M 1 2

DNA Lab, Columbia University Question Viewer’s available bandwidth fluctuates How do we download? The two extremes: Emphasize present quality Ensure future first The tradeoff Bandwidth utilization vs Variation in quality Bandwidth Video layers Current time New Bandwidth Present Future

DNA Lab, Columbia University Structure Model Problem Formulation Ideal solution (offline) Online Solutions Naïve Solutions Hill-climbing Solutions Results

DNA Lab, Columbia University Discretizing the Video Like to deal with video in constant sized units But video is variable rate Divide time into variable-length epochs Size epochs to have same number of video bits S Each layer of each epoch is termed a chunk All chunks are of the same size (S/M bits) Time t Pre-fetch Epoch C1 C2 C3 Playback Bitrate Epoch 0 Equal Areas (S bits) Equal Areas (S/M bits) Epoch 1 M=3

DNA Lab, Columbia University The Model(2) Time t Downloaded chunks Current Epoch The number of chunks of video downloaded in each epoch varies Epoch lengths vary Bandwidth varies Bandwidth of current epoch is used to download video chunks for future epochs Available Bandwidth (3 chunks)

DNA Lab, Columbia University Discrete Model Playback Output: A= the allocation vector a i is the total #chunks allocated to epoch i 0 ≤ a i ≤ M Bandwidth Input: W= the bandwidth vector w i is the #chunks of bandwidth available at epoch i Allocation 012 Bandwidth W= A= 341

DNA Lab, Columbia University An Example Which future chunk should be downloaded with a chunk of bandwidth currently available ? 34 1 Bad Varying quality, wasted bandwidth 012 Bandwidth Good Even quality, Little waste waste M=3 Example 1Example 2

DNA Lab, Columbia University Metrics of Performance Waste Unused bandwidth (all future chunks already downloaded) Σw i - Σ a i Variability Variance from maximum possible quality (layers) Σ (M – a i ) 2 Smoothness Absolute change in quality Σ Abs(a i-1 -a i ) Goal: Minimize these metrics Variability Waste Smoothness

DNA Lab, Columbia University Needed Given: Bandwidth vector Produce: Allocation vector that Minimizes Waste, Smoothness, Variability Under Constraints Quality: Bandwidth/Time: Online: w i needs to be allocated before w i+1

DNA Lab, Columbia University Structure Model Problem Formulation Ideal solution (offline) Online Solutions Naïve Solutions Hill-climbing Solutions Results

DNA Lab, Columbia University The optimal solution (offline) Is Given all of W Allocates bandwidth of last epoch w T first and works its way back to epoch 1 Allocates w i to the smallest, latest non- full (a j <M) epoch Proved to minimize waste, smoothness and variability in paper 012 Bandwidth M=

DNA Lab, Columbia University Structure Model Problem Formulation Ideal solution (offline) Online Solutions Naïve Solutions Hill-climbing Solutions Results

DNA Lab, Columbia University Online Solutions (naïve) Online algorithms make decisions about w i purely based on w j,0≤j<i Same-Index Allocates all bandwidth to earliest future epoch Non-smooth, high- variability, low-waste Smallest Bin Allocates all bandwidth to most empty epoch Downloads all of layer-1, then all of layer-2, etc. Smoother, high-waste W: Waste(2) M=

DNA Lab, Columbia University Hill-climbing online solutions Solution: smooth and low-waste Guideline Experiments: Viewers extremely sensitive to abrupt lowering of video quality. Solution: algorithms that Bound the downhill slope of allocations (decrease in quality) Then maximize current quality

DNA Lab, Columbia University Largest-Hill Maximize current quality but ensure gentle fall in quality Allocates such that a j -a j+1 <C Produces hills with gentle downhill slopes 012 Bandwidth C=1 M=4

DNA Lab, Columbia University Structure Model Problem Formulation Ideal solution (offline) Online Solutions Naïve Solutions Hill-climbing Solutions Results

DNA Lab, Columbia University Results from simulation How do the algorithms perform under strain Video bitrate approach bandwidth Bandwidth fluctuation increases Draw bandwidth from Uniform and Normal distributions 600 epochs 100 runs

DNA Lab, Columbia University Results from bandwidth traces Bandwidth traces from DSL Line Trace lasted 11,682 secs Downloaded 80 Mb video 1,2 or more servers Performance as function of epoch-lengths (1,2,4,..,128) secs

DNA Lab, Columbia University Conclusion A solution that allows live video streaming on P2P networks Uses scalable-coding to overcome insufficient-bandwidth problem slice stream into lower-bandwidth streams Clever pre-fetching ensures consistent high-quality Provide optimal offline solution Our online-algorithms performs close to optimal