Prediction-Driven Mobile Broadcast for Cellular Networks
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Solution Overview
Problem
In wireless networks, the transition of user equipment (UEs) from point-to-point (P2P) to point-to-multipoint (P2MP) data traffic for video consumption is hindered by the broadcaster's inability to predict demand accurately, resulting in unrealized bandwidth efficiency benefits.
Innovation Solution
A prediction-driven mobile broadcast system that utilizes sensors to determine data consumption information, correlators to generate demand statistics, and demand prediction engines to forecast future demand for content data sets, thereby optimizing broadcast schedules to align with predicted demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If broadcasters provide P2MP broadcast content, then network bandwidth demand is reduced, but UEs will only switch from P2P to P2MP if demand prediction is sufficiently accurate to lure them
Solution Approach 1:
The system performs preliminary demand prediction and analysis before broadcasting content. The demand prediction engine analyzes historical broadcast data, user preferences, and contextual information in advance to predict future content demand, allowing broadcasters to prepare and schedule broadcasts before users actually need the content, thus enabling proactive P2MP broadcasting that can attract users away from P2P streaming.
Solution Approach 2:
The system implements a feedback mechanism where demand prediction results are continuously refined based on actual broadcast performance and user reception data. The demand prediction engine uses feedback from previous broadcasts to improve its accuracy over time, creating a closed-loop system that progressively enhances demand forecasting capability and increases user confidence in P2MP content availability.
2Productivity
If broadcasters rely on accurate demand prediction to schedule broadcasts, then P2MP efficiency is maximized, but the complexity of predicting future content demand increases
Solution Approach 1:
The demand prediction system is segmented into multiple independent functional modules: a data collection module that gathers user preferences and broadcast history, a demand prediction engine that processes this data, and a broadcast scheduling module that acts on predictions. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high P2MP broadcast efficiency.
Solution Approach 2:
The patent introduces intermediary components between raw data and final broadcast decisions, including a demand prediction engine that acts as a mediator between user behavior data and broadcast scheduling. This intermediary layer processes and interprets complex user data patterns, transforming them into actionable broadcast schedules without requiring direct complex interactions between all system components.
3Productivity
If more UEs consume P2MP video content, then network resource allocation is optimized, but this requires convincing UEs to swap from P2P to P2MP consumption
Solution Approach 1:
The system implements dynamic broadcast scheduling that adapts to changing user demands in real-time. The demand prediction engine continuously updates its forecasts based on current user behavior patterns, and the broadcast schedule is dynamically adjusted to match predicted demand. This dynamic approach maintains UE flexibility to access desired content while progressively steering users toward P2MP consumption as prediction accuracy improves.
Solution Approach 2:
The system changes key parameters such as broadcast timing, content selection, and quality levels based on predicted user demand. By dynamically adjusting these parameters according to prediction results, the system optimizes network resource allocation for P2MP broadcasts while maintaining user satisfaction and flexibility, making the transition from P2P to P2MP consumption attractive to users.
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AI summary
Solutions are disclosed that provide for prediction-driven mobile broadcast in order to increase the number of user equipment (UEs) that move from point-to-point (P2P) data traffic to point-to-multipoint (P2MP) for video consumption. This improves the demand for broadcast video by ensuring that more popular content is broadcast. Examples include: a sensor operable to determine, using a cellular uplink, data consumption information for a plurality of user equipment (UEs) receiving a broadcast; a correlator operable to correlate the data consumption information with content identification in a first broadcast schedule into demand statistics; and a demand prediction engine operable to generate demand predictions based on at least the demand statistics, wherein the demand predictions indicate a future demand for content data sets in a future time period.