Client-Side Scheduling for Media Transmissions
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Solution Overview
Problem
Mobile devices experience varying network capabilities as they move through different network segments, leading to inconsistent data transmission rates due to changing bandwidth and power state transitions, which existing technologies fail to efficiently manage.
Innovation Solution
A method and device that predict future network and power state characteristics based on historical data and current conditions, allowing for client-side scheduling of data transmissions to optimize data packet requests and reduce power consumption by rescheduling requests to align with anticipated power state transitions and higher bandwidth availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data transmission requests are sent immediately without scheduling, then response time is reduced, but power consumption increases due to frequent power state transitions
Solution Approach 1:
The system performs preliminary actions by predicting future power states and network conditions before actually sending data transmission requests. The client device schedules requests in advance to align with predicted high-bandwidth periods and stable power states, avoiding immediate transmission that would trigger unnecessary power state transitions. This resolves the contradiction by preparing transmission schedules beforehand based on predictions, thus reducing both response time and power consumption.
2Productivity
If data transmission requests are scheduled based on network conditions, then data transmission efficiency is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the client device continuously monitors actual power states and network conditions, then uses this feedback to refine future predictions and scheduling decisions. The predicted power states and network conditions are adjusted based on historical data and actual outcomes, creating a closed-loop system that improves transmission efficiency without requiring overly complex scheduling mechanisms. The feedback loop allows the system to learn and adapt, resolving the contradiction between efficiency and complexity.
3Use of energy by moving object
If power state transitions are reduced to save energy, then power consumption decreases, but data delivery timeliness worsens
Solution Approach 1:
The system applies dynamics by making the scheduling strategy adaptive rather than static. The client device dynamically adjusts transmission schedules based on predicted power states and network conditions, allowing it to optimize between power consumption and timeliness in real-time. When high bandwidth and stable power states are predicted, the system schedules transmissions to meet timeliness requirements. When conditions are less favorable, it adjusts schedules to minimize power consumption. This dynamic adaptation resolves the contradiction between reducing power transitions and maintaining data delivery timeliness.
Data Source
AI summary
A mobile device that incorporates the subject disclosure may perform, for example, obtaining performance characteristics for network segments of a network where the network segments are selected from a group of network segments of the network based on a trajectory of the mobile device. The mobile device can monitor power state transitions of the mobile device, and can predict a future power state of the mobile device based on the monitoring of the power state transitions. The mobile device can determine a target time for sending a request for transmission of a data packet over the network where the target time is determined based on the performance characteristics for the network segments and based on the future power state of the mobile device. The mobile device can schedule a time for sending the request for transmission according to the target time. Other embodiments are disclosed.