Optimizing the Quality of Scalable Video Streams in P2P Networks Impact Method Viewing scalably encoded videos in P2P systems without smart prefecthing strategies yields a poor viewing experience We provide an off-line algorithm that provides the optimal performance given bandwidth constraints We provide on-line algorithms that perform close to the optimum and vastly outperform naïve algorithms The Problem Raj Kumar Rajendran Dan Rubenstein Discretized Model: Video time-sliced fixed-bytesize epochs How do we use available bandwidth chunks of current epoch? Novel Prefetching Approach: We identify an off-line algorithm when future bandwidth availability is known We prove optimality: –Minimizes waste and variability –Maximizes smoothness –In practice: future bandwidth availability not known We develop on-line algorithms that allocates current bandwidth to download current or future parts of the video Bandwidth Trace Experiments: Experiment –Input: bandwidth traces obtained while downloading video from a P2P network –Tested on DSL and T1 –Video downloaded from multiple peers –Waste, smoothness, Variabilty measured with increasing epoch lengths Results –Mean-hill and wide hill allocators perform close to the bound –Largest hill performs a little worse –Naïve Allocators perform poorly Simulation Results Experiment –Input bandwidth simulated –Increasing variance, constant mean –Waste,smoothness, variability measured Results –Constrained Allocators vastly outperform naïve allocators and are close to the bound –The naïve allocators perform well on one of the measures but poorly overall Video streaming in P2P systems is a potential killer app Bandwidth availability to a client fluctuates unpredictably and rapidly with time Solution: use scalable coding (FGS) to break up video into M layers Viewing more layers = higher fidelity viewing Challenge: what should available bandwidth be used to prefetch? –Downloading all layers of current portion can create unpleasant variation in video quality as bandwidth availability changes –Prefetching one layer at a time unnecessarily forces viewing of initial portion of video at lowest quality Experimental Verification Formulate measures of video quality –Waste: the amount of unused available bandwidth –Smoothness: the average change in video quality –Variability: the standard deviation How do we maximize video quality time- varying bandwidth? Bitrate Time Video bitrate Bandwidth Epoch 1Epoch 5 Buffering Solution Naïve Solutions Same-Index (Greedy) –Allocate all currently available bandwidth to nearest future epoch –Problem: Large variation in the quality of video displayed Smallest-Bin (Conservative) –Allocate all current bandwidths to future epoch with fewest layers –Problem: Wastes bandwidth Our Solution: Constrained Allocators Attempt to maximize utilization, given smoothness constraints Bound the downhill slope of allocations Three variants Video Quality of Constrained Allocators Largest Hill: Allocate to earliest epoch maintaining slope Mean Hill: Most empty epoch smaller than mean, maintaining slope Wide Hill: Earliest epoch smaller than mean maintaining slope Bandwidth from a single Epoch Approach Dept of Electrical Engineering Columbia University