Throughput Estimation for Adaptive Video QoE
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Solution Overview
Problem
Existing techniques struggle to accurately estimate the required throughput for achieving a certain Quality of Experience (QoE) in adaptive bit rate video distribution, as they fail to consider differences in representation and limitations in selectable bit rates.
Innovation Solution
A throughput estimation apparatus that includes a QoE estimation unit to estimate QoE for various representation candidates and a throughput estimation unit to calculate the required throughput based on the estimated QoE, representation parameters, and target QoE.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques use only throughput as input to estimate QoE, then the estimation process is simple, but the accuracy deteriorates because representation differences and selectable bit rate limitations are not considered
Solution Approach 1:
The estimation process is segmented into multiple stages: first estimating QoE for each representation candidate individually, then using these individual estimates to determine the overall throughput requirement. This segmentation allows consideration of representation differences while maintaining a structured, manageable estimation process.
Solution Approach 2:
The approach transitions from a single-dimensional throughput estimation to a multi-dimensional estimation that incorporates both throughput and representation characteristics. By estimating QoE across multiple representation candidates and considering their individual contributions, the system achieves more accurate throughput estimation that reflects real-world adaptive bit rate scenarios.
2Reliability
If excessive throughput is provided to satisfy target QoE, then QoE requirements are met, but network equipment cost increases
Solution Approach 1:
The system dynamically determines the required throughput parameter based on actual QoE estimates for different representations. By changing the throughput parameter to match the actual requirements derived from QoE estimation, rather than using a fixed excessive throughput value, the system achieves cost-efficient network design that satisfies QoE requirements without over-provisioning.
Solution Approach 2:
The estimation process incorporates feedback loops where QoE estimates for different representations are used to adjust and refine the throughput determination. This feedback mechanism ensures that the final throughput value is optimized to meet QoE requirements precisely, avoiding both under-provisioning and excessive over-provisioning that would increase costs.
Data Source
AI summary
A throughput estimation apparatus includes a memory and a processor configured to execute estimating a QoE (quality of experience) for each of a plurality of selection candidates for a parameter set related to a quality of a video to be distributed via a network; and estimating a throughput required for satisfying a target QoE by using, as inputs, the estimated QoE for each of the selection candidates, the parameter set for each of the selection candidates, and the target QoE.