How to Calculate QOE in omnet++

To calculate the Quality of Experience (QoE) in OMNeT++ has needs to measure the user satisfaction with network services especially for applications such as video streaming, VoIP, or online gaming. The term QoE commonly connects with network performance metrics like latency, jitter, packet loss, and throughput, but also considers subjective user experiences. The given below will help you to calculate the QoE in OMNeT++:

Steps to Calculate QoE in OMNeT++:

1. Set Up the Network Model:
   • Describe network topology using the NED language. This contains to setting up nodes like clients and servers, links, and communication protocols.
   • Configure the physical and MAC layers based on the type of application such as video streaming, VoIP.
2. Implement the Application Layer Traffic:
   • Use application-level modules (or create own) to mimic the traffic relevant to the service for evaluating the video streaming, VoIP.
   • For video streaming, we need to emulate traffic using CBR (Constant Bit Rate) or VBR (Variable Bit Rate) patterns.
3. Measure QoS Metrics:
   • Latency (Delay): The time it takes for a packet to travel from the source to the destination.
   • Jitter: The variation in packet arrival times.
   • Packet Loss: The percentage of packets lost during transmission.
   • Throughput: The rate of successful data delivery over the network.
4. Model the QoE Based on QoS Metrics:
   • Implement a mathematical model to convert QoS metrics into a QoE score. Common methods contain:
     • Mean Opinion Score (MOS): A subjective score for voice or video quality usually ranging from 1 (bad) to 5 (excellent).
     • Video Quality Metrics (e.g., PSNR, SSIM): For video streaming, these metrics evaluate the quality of the received video compared to the original.
     • Custom QoE Models: Based on empirical information or literature, we need to develop models that map QoS metrics to user satisfaction.
5. Implement the QoE Calculation:
   • Extend or generate a module in OMNeT++ that estimates the QoE score based on the collected QoS metrics. This can be completed using a simple mathematical formula or a more complex model depending on the application.

Example: QoE Calculation for VoIP using MOS

In the below is the instance of how to calculate a simple MOS (Mean Opinion Score) for a VoIP application:

#include <omnetpp.h>

using namespace omnetpp;

class VoipClient : public cSimpleModule {

private:

simtime_t lastSendTime;

simtime_t lastReceiveTime;

int packetsSent;

int packetsReceived;

double totalDelay;

double totalJitter;

int packetLoss;

protected:

virtual void initialize() override {

lastSendTime = SIMTIME_ZERO;

lastReceiveTime = SIMTIME_ZERO;

packetsSent = 0;

packetsReceived = 0;

totalDelay = 0;

totalJitter = 0;

packetLoss = 0;

scheduleAt(simTime() + par(“sendInterval”).doubleValue(), new cMessage(“send”));

}

virtual void handleMessage(cMessage *msg) override {

if (msg->isSelfMessage()) {

// Simulate sending a VoIP packet

lastSendTime = simTime();

packetsSent++;

send(msg, “out”);

} else {

// Simulate receiving a VoIP packet at the destination

simtime_t receiveTime = simTime();

packetsReceived++;

// Calculate delay and jitter

simtime_t delay = receiveTime – lastSendTime;

totalDelay += delay.dbl();

if (lastReceiveTime != SIMTIME_ZERO) {

simtime_t jitter = fabs((receiveTime – lastReceiveTime).dbl() – (lastSendTime – lastSendTime).dbl());

totalJitter += jitter;

}

lastReceiveTime = receiveTime;

delete msg;

}

// Schedule the next packet send

scheduleAt(simTime() + par(“sendInterval”).doubleValue(), new cMessage(“send”));

}

virtual void finish() override {

// Calculate average QoS metrics

double averageDelay = totalDelay / packetsReceived;

double averageJitter = totalJitter / (packetsReceived – 1);

packetLoss = packetsSent – packetsReceived;

// Calculate MOS

double mos = CalculateMOS(averageDelay, averageJitter, packetLoss);

EV << “Mean Opinion Score (MOS): ” << mos << “\n”;

}

double CalculateMOS(double delay, double jitter, int packetLoss) {

double rFactor = 94.2 – (delay / 10.0) – (jitter / 5.0) – (2.5 * packetLoss);

if (rFactor < 0) rFactor = 0;

return 1 + 0.035 * rFactor + 7.0e-6 * rFactor * (rFactor – 60) * (100 – rFactor);

}

};

Define_Module(VoipClient);

Explanation:

• VoipClient Module:
  • To mimic a VoIP client sending and receiving packets.
  • Estimate QoS metrics like delay, jitter, and packet loss during the simulation.
• CalculateMOS Function:
  • Execute a simple model to estimate the Mean Opinion Score (MOS) based on the QoS metrics. This function converts the delay, jitter, and packet loss into a user-perceived quality score.

Additional Considerations:

• Video QoE: For video streaming applications, we usually use metrics such as PSNR (Peak Signal-to-Noise Ratio) or SSIM (Structural Similarity Index) to evaluate the quality of the received video stream compared to the original.
• Subjective Testing: Real QoE assessment usually includes subjective testing, where users rate their experience. While OMNeT++ simulations are objective, we need to map objective metrics to QoE scores based on empirical data from such studies.
• Advanced Models: Depending on application, we need to use more advanced QoE models that consider more factors such as user expectations, device capabilities, and network conditions.

We clearly understood how to calculate and estimate the parameter that relates the QOE to satisfy the user experience in the tool of OMNeT++. Additional details were provided about the QOE in further additional module.

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