Edge Node Adaptive Bitrate Algorithm for Video Fairness
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Solution Overview
Problem
Existing adaptive bitrate algorithms for video streaming lack fairness among clients due to independent processing at each device, leading to suboptimal Quality of Experience (QoE) and inefficient resource allocation in mobile networks.
Innovation Solution
Deploying an adaptive bitrate algorithm at edge nodes that computes a weighted sum score for video segment qualities based on quality and fairness scores, allowing for on-the-fly adjustments and prefetching to optimize segment delivery, leveraging edge computing capabilities for improved latency and fairness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If client-based adaptation is used, then scalability is improved, but fairness among clients deteriorates
Solution Approach 1:
The patent introduces an edge server as an intermediary between clients and the content delivery network. This edge server receives segment requests from multiple clients, computes weighted sum scores considering both quality and fairness metrics, and generates modified requests that balance individual quality preferences with overall network fairness. This mediator approach maintains the scalability of client-based adaptation while improving fairness through centralized coordination at the edge.
2Reliability
If multiple coordination proxies are deployed, then fairness is improved, but device complexity increases
Solution Approach 1:
The patent merges the fairness coordination function into a single edge server that handles multiple client requests. Instead of deploying multiple coordination proxies throughout the network architecture, the edge server consolidates the fairness computation and request modification functions, reducing network architecture complexity while maintaining fairness among clients.
3Productivity
If resource allocation is managed at base station, then network utility is optimized, but control flexibility is reduced
Solution Approach 1:
The patent shifts the resource allocation control from the base station dimension to the edge server dimension. The edge server operates in a different architectural layer, closer to the clients, allowing it to optimize network utility through weighted sum score computation while maintaining control flexibility through on-the-fly adaptation of segment requests. This dimensional shift enables both network utility optimization and application-level control flexibility.
4Reliability
If segment requests are collected in time slots, then coordination is improved, but latency increases
Solution Approach 1:
The edge server performs preliminary computation of weighted sum scores and generates modified segment requests before the actual content delivery. By pre-computing the optimal quality selections considering fairness metrics, the system reduces coordination delays and enables faster segment delivery without sacrificing coordination among clients.
Data Source
AI summary
The technology described herein relates to implementing an adaptive bitrate (ABR) algorithm at edge nodes. A method for implementing an ABR algorithm at an edge node may include receiving at the edge node a request for a video segment from a client according to the client's ABR algorithm, the request indicating a quality. A weighted sum score for each of a set of qualities may be computed based on a quality score and a fairness score using the ABR algorithm at the edge node, the qualities including at least the requested quality and another quality. A modified request may be generated in response to the weighted sum score for the other quality being better than the weighted sum score for the requested quality. The modified request may be sent to a server. The video segment in the other quality may be received from the server and provided to a client.