Network-Based DASH Proxy Rate Determination
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Solution Overview
Problem
Dynamic Adaptive Streaming over HTTP (DASH) clients face challenges in maintaining high user quality of experience (QoE) due to fast-changing wireless channel quality and the inability to accurately measure throughput and buffer conditions, leading to suboptimal video rate determination and increased network load management difficulties for Wireless Service Providers.
Innovation Solution
Implementing a network-based rate determination system that accurately measures wireless network throughput and DASH client cache buffer size and fullness, allowing for informed video segment rate selection and buffer management, thereby enhancing QoE and network load management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If client-based rate determination is used, then the system is simple to implement, but measurement precision of throughput and buffer conditions deteriorates
Solution Approach 1:
The patent introduces a network-based DASH proxy as an intermediary between the client and content server. This proxy accurately measures throughput by timing video segment downloads and estimates buffer fullness by analyzing request patterns, providing precise measurements that neither pure client-based nor pure server-based solutions can achieve alone.
2Measurement precision
If network-based rate determination is implemented, then measurement precision of throughput and buffer conditions improves, but device complexity increases
Solution Approach 1:
The DASH proxy leverages existing HTTP protocol mechanisms and readily available network timing data to perform measurements. It uses standard HTTP headers, existing download timing information, and request pattern analysis without requiring specialized hardware or complex proprietary protocols, thus achieving accurate measurements with moderate complexity.
3Adaptability or versatility
If client controls video rate selection, then adaptability to local conditions improves, but network load management capability deteriorates
Solution Approach 1:
The system implements a feedback loop where the DASH proxy continuously monitors network throughput and buffer conditions, then adjusts video segment rate recommendations accordingly. This closed-loop control enables both local adaptation to client conditions and centralized network load management by the proxy based on overall network state.
4Reliability
If video segment rate is increased, then user quality of experience improves, but network bandwidth consumption increases
Solution Approach 1:
The system dynamically adjusts video segment rates based on real-time network conditions and buffer state. The DASH proxy continuously updates throughput measurements and buffer fullness estimates, then selects video rates that adapt to changing conditions, ensuring high QoE when network capacity permits while conserving bandwidth when resources are constrained.
Data Source
AI summary
In one example embodiment, a method of adaptive video streaming includes receiving, by a server, a request for a video segment from an end user device and determining, by the server, a plurality of variables, the plurality of variables including at least one of a download time of a previously requested video segment by the end user device, a fullness of a buffer at the end user device and a size of the buffer. The method further includes determining, by the server, a rate of the requested video segment based on one or more of the determined plurality of variables and sending, by the server, the requested video segment at the determined rate to the end user device.