Adaptive Video Streaming Distortion Estimation
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Solution Overview
Problem
Current methods for estimating end-to-end distortion in pre-compressed video streams are inaccurate due to the lack of network status information at compression time and the inability to account for inter-frame error propagation and error concealment, leading to suboptimal adaptive strategies.
Innovation Solution
A method that generates side-information during compression, storing it with the pre-compressed data, allowing for accurate end-to-end distortion estimation at delivery time, considering quantization, packet loss, and error propagation, using a first-order distortion estimation algorithm with low computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If pre-compressed video is transmitted without side-information, then transmission bandwidth is reduced, but distortion estimation accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by generating and storing side-information about the original video data during the compression phase, before transmission. This side-information includes statistical characteristics and distortion metrics that will be used later at transmission time to accurately estimate end-to-end distortion without needing to transmit the actual original video data, thus resolving the contradiction between bandwidth efficiency and estimation accuracy
Solution Approach 2:
The patent uses copying by creating a compressed representation (side-information) of the original video data's statistical properties rather than transmitting the original data itself. This copied information suffices for distortion estimation purposes, achieving accurate distortion assessment while minimizing transmission bandwidth requirements
2Measurement precision
If complex distortion estimation methods are used, then estimation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential statistical characteristics and distortion-related information from the original video data during compression, storing them as compact side-information. At transmission time, distortion estimation is performed using only this extracted information combined with simple network status parameters, avoiding the need for complex full-frame reconstruction and comparison operations while maintaining adequate estimation accuracy
Solution Approach 2:
The patent changes parameters by using pre-computed statistical parameters and distortion metrics stored in side-information rather than performing real-time complex computations. The estimation method transforms the problem from computing full distortion to using stored parameters with simple network status inputs, significantly reducing computational complexity while providing sufficient estimation accuracy for adaptive transmission decisions
3Adaptability or versatility
If network status information is not available at compression time, then pre-compressed video can be stored for later delivery, but adaptive delivery optimization is lost
Solution Approach 1:
The patent implements feedback by incorporating real-time network status information (packet loss rate, bandwidth availability) at transmission time into the distortion estimation process. The side-information about original video characteristics is combined with current network conditions to dynamically adjust transmission parameters and select appropriate error protection strategies, enabling adaptive optimization despite the video being pre-compressed
Solution Approach 2:
The patent applies dynamics by making the distortion estimation and transmission parameter selection adaptive to changing network conditions. Although the video is pre-compressed, the system dynamically adjusts error protection levels, retransmission strategies, and quality parameters based on real-time network status feedback, transforming a static pre-compressed stream into a dynamically optimized delivery system
Data Source
AI summary
A method is described for efficiently determining total end-to-end distortion of a pre-compressed data stream, such as video streams or other media streams, at the time of delivery over a lossy-network, and for providing adaptive error-resilient delivery schemes based on distortion estimates. The methods can be utilized with single or multilayer packet streams and are particularly well suited for video streams. By way of example, distortion estimates are performed by generating side-information at the time of data stream compression, wherein the side-information is used in conjunction with information about the network status to determine an estimated distortion for the group of packets when the data stream is transported over the network to a destination end. This estimation may be utilized within described resiliency techniques in which the error correction mechanism is selected in response to the estimated distortion, which may be additionally refined in reference to cost factors.