Server-Side Media Scheduling for Client Power States
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Solution Overview
Problem
Current media distribution systems face challenges in managing media transmissions efficiently, particularly in adapting to client device power state transitions and network performance variations, leading to potential delays and quality degradation in media content delivery.
Innovation Solution
A server-side scheduling scheme that predicts future power states and network performance characteristics of client devices, allowing for dynamic rescheduling of media segment transmissions to ensure optimal delivery quality by adjusting transmission times and bitrates based on anticipated device states and network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If media transmissions are scheduled without considering client device power states, then network resources are utilized efficiently, but media delivery quality degrades due to power state transitions and interruptions
Solution Approach 1:
The server predicts future power states of client devices before scheduling media transmissions. By determining anticipated power states in advance and proactively adjusting transmission schedules to align with high-power states, the system prevents delivery interruptions before they occur, thereby improving reliability without requiring complex real-time adjustments
Solution Approach 2:
The system implements a feedback mechanism where the server receives power state information from client devices, analyzes this data to predict future power states, and uses these predictions to dynamically adjust transmission schedules. This closed-loop approach enables adaptive scheduling that responds to actual device conditions while maintaining manageable system complexity through standardized feedback protocols
2Reliability
If media segments are transmitted continuously without rescheduling, then transmission speed is maintained, but network performance degrades due to varying network conditions and device power states
Solution Approach 1:
The scheduling system transitions from static to dynamic operation by continuously monitoring power state transitions and network conditions. The server adapts transmission schedules in real-time based on predicted power states, dynamically adjusting transmission timing to ensure segments arrive during high-power states while maintaining overall transmission efficiency and minimizing delays
Solution Approach 2:
The server performs preliminary analysis of power state patterns and network conditions to predict future states before scheduling transmissions. By determining optimal transmission windows in advance based on predicted high-power states and good network conditions, the system proactively schedules transmissions to avoid delays and interruptions rather than reacting to problems after they occur
Data Source
AI summary
A system embodying the subject disclosure includes a memory to store instructions. A controller coupled to the memory, responsive to executing the instructions, can perform operations including obtaining performance characteristics for segments of a network; the segments are selected based on a trajectory of a mobile device coupled to the network. The controller can predict future transport and segment characteristics based on the performance characteristics. The controller can receive a request from the mobile device for transmission of a data packet over the network. The controller can monitor a power state of the mobile device and predict a future power state of the mobile device. The controller can determine a target time for fulfilling the request, based on the future power state and the future transport and segment characteristics. The controller can schedule a time for fulfilling the request according to the target time. Other embodiments are disclosed.