Rate Limiter Stabilizes Adaptive Streaming Video Clients
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Solution Overview
Problem
Adaptive bitrate streaming clients experience instability when competing for bandwidth, leading to frequent shifts in video rate and a degradation in viewer quality of experience due to misestimation of available bandwidth and complex feedback patterns among clients.
Innovation Solution
Implementing a rate limiter that dynamically sets and adjusts the encoding rate limit for each client to allow upshifts while maintaining overlapping transfers, thereby stabilizing video rates and improving the overall quality of experience without centralized management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple adaptive bitrate streaming clients compete for bandwidth without rate limiting, then each client can independently adjust its video rate, but the system experiences instability with frequent rate shifts and degradation in overall quality of experience
Solution Approach 1:
The patent introduces a rate limiter as an intermediary component that mediates between multiple adaptive bitrate clients and the network bandwidth. The rate limiter receives rate requests from clients, applies rate limiting logic to prevent excessive rate changes, and returns approved rates to clients. This intermediary mechanism stabilizes the system by filtering out erratic rate adjustments while still allowing clients to adapt to bandwidth changes, thus resolving the contradiction between adaptability and stability.
Solution Approach 2:
The rate limiter implements a feedback mechanism where it monitors the current rates of multiple clients, their requested rate changes, and network conditions. Based on this feedback, the rate limiter dynamically adjusts approved rates for each client, preventing situations where clients make aggressive rate changes that cause system instability. The feedback loop ensures that rate adjustments are coordinated across clients, maintaining overall system stability while preserving individual client adaptability.
2Adaptability or versatility
If clients frequently adjust their requested video rate in response to bandwidth changes, then clients can adapt to available bandwidth, but the frequent rate changes cause distracting quality level shifts and bias toward lowest video rates
Solution Approach 1:
The rate limiter acts as a mediator between client bandwidth adaptation requests and the actual video rate changes. It evaluates each client's adaptation request against current network conditions and the states of other clients, approving only those rate changes that are appropriate. This prevents clients from making excessive or inappropriate rate adjustments, thereby maintaining reliable quality of experience while still allowing necessary bandwidth adaptation.
Solution Approach 2:
The rate limiter dynamically adjusts its limiting behavior based on current system conditions. When bandwidth is abundant and stable, it allows clients to adapt more freely. When bandwidth is constrained or fluctuating, it applies stricter rate limiting to prevent destabilizing changes. This dynamic approach maintains quality of experience reliability across varying network conditions while preserving client adaptability when appropriate.
3Stability of the object's composition
If a rate limiter is introduced to stabilize video rates, then rate changes are reduced and quality of experience improves, but the device complexity increases due to additional rate limiting logic
Solution Approach 1:
The rate limiter is implemented as a separate intermediary component rather than embedding complex control logic within each client device. This modular approach centralizes the complexity in a dedicated rate limiting module that can be implemented in the network infrastructure or as a standalone service, keeping individual client devices simple while achieving system-wide rate stabilization.
Solution Approach 2:
The rate limiter manages complexity by focusing on controlling key parameters (client video rates) rather than managing all system states. It uses parameter-based rate limiting where it monitors and adjusts specific rate parameters for each client based on simplified rules and current system state, achieving stabilization without requiring complex algorithms in each client device.
Data Source
AI summary
In one embodiment, a method includes identifying a current encoding rate requested by a client device for content received from a content source, setting at a network device a rate limit to limit the rate at which the content is received at the client device based on the current encoding rate, and adjusting the rate limit based on changes in the current encoding rate. The rate limit is set to allow the client device to change the current encoding rate to a next higher available encoding rate.