Wireless Media Flow Prediction for Adaptive FEC in LTE Cells
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Solution Overview
Problem
In wireless communication networks, especially in mobile LTE networks, existing error correction methods like adaptive FEC struggle to adapt quickly to changing network conditions, leading to inefficiencies in real-time media streaming due to delayed and inaccurate feedback from receivers, which affects the quality of experience for users.
Innovation Solution
A method and system that predict future wireless network conditions by collecting and analyzing radio network statistics from multiple UEs in a cell, allowing for the determination of predicted throughput and packet loss for a specific UE, and sending this information to the sender for optimal data rate and error correction selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If receiver-based feedback methods are used for adaptive FEC, then the system can adapt to network conditions, but the adaptation is slow and inaccurate due to delayed feedback
Solution Approach 1:
Instead of having the receiver measure and report network conditions back to the sender (traditional feedback approach), the patent inverts the approach by having the network node (e.g., base station) directly measure and predict network conditions for the sender. This eliminates the feedback delay and accuracy issues inherent in receiver-based methods, as the network node has direct access to current radio network statistics and can provide real-time predictions to the sender.
Solution Approach 2:
The patent introduces the network node (base station) as an intermediary between the sender and receiver. The network node collects radio network statistics, predicts throughput and packet loss, and provides this information to the sender. This intermediary approach allows for more accurate and timely network condition information compared to direct receiver-to-sender feedback, as the network node has privileged access to network state information.
2Ease of manufacture
If static FEC is used, then implementation is simple, but it causes excessive overhead when error rate is low
Solution Approach 1:
The patent implements dynamic FEC by allowing the sender to adjust the FEC redundancy rate based on predicted network conditions. Instead of using a fixed static FEC configuration, the system dynamically adapts the level of error correction applied to match current network conditions. When network conditions are good (low predicted packet loss), less FEC redundancy is applied, reducing overhead. When conditions deteriorate, more redundancy is applied, maintaining reliability.
Solution Approach 2:
The patent changes the FEC parameter (redundancy rate) based on predicted network conditions. The sender receives predicted throughput and packet loss information from the network node and adjusts the FEC configuration accordingly. This parameter adaptation allows the system to optimize between overhead and error protection, using minimal FEC when not needed and maximum FEC when necessary, resolving the contradiction between simple static implementation and efficient dynamic adaptation.
3Measurement precision
If receiver-based packet loss prediction is used, then the system can estimate network conditions, but the prediction is not accurate enough for optimal FEC selection
Solution Approach 1:
The network node serves as an informed intermediary that has direct access to radio network statistics, including current load, radio quality metrics, and historical performance data. By having this privileged information source provide predictions directly to the sender, the system achieves much higher prediction accuracy compared to receiver-based methods that only have access to limited feedback information. The network node can correlate multiple UEs' experiences and provide statistically robust predictions.
Solution Approach 2:
The patent implements a feedback mechanism where the network node continuously monitors radio network statistics and provides updated predictions to the sender. This feedback loop ensures the sender has current and accurate network condition information for FEC selection. The feedback is more accurate than receiver-based methods because it comes from the network node that directly controls and measures the radio interface conditions.
Data Source
AI summary
Disclosed is a method performed by a system of a wireless communication network for determining transmission conditions for a real-time media flow to be transmitted wirelessly to a first User Equipment, UE, residing in a first cell served by a first base station of the network. The method comprises obtaining radio network statistics of the first cell, the radio network statistics comprising radio network data for individual of a plurality of UEs in the first cell, UE throughput for individual of the plurality of UEs, and UE packet loss or packet delay for individual of the plurality of UEs, and obtaining, at a second time point occurring later in time than the obtaining of the radio network statistics, radio network data for the first UE. The method further comprises determining a predicted first UE throughput and a predicted first UE packet loss or packet delay based on the obtained radio network statistics of the first cell and on the obtained radio network data for the first UE, and triggering sending of information of the predicted first UE throughput and of the predicted first UE packet loss or excessive packet delay to a sender of the real-time media flow to the first UE. A Forward Error Correction, FEC, optimization module takes the predicted UE packet loss rate, the predicted UE throughput, and adapts the amount of FEC redundancy packets according to the throughput and packet loss rate. Further, the adapted amount of FEC redundancy packets are then used by the sender of the video when transmitting the video.