How to Implement Network QoE Attainment in NS2

To implement the Network Quality of Experience (QoE) in ns2 which means the end-user’s observed experience with a service or network, commonly used in multimedia and real-time services like video streaming, VoIP, and gaming. executing QoE attainment in NS2 has include to mimic a network environment with focus on key metrics that impacts QoE, like bandwidth, latency, jitter, packet loss, and ultimately the subjective user experience.

To execute QoE in NS2, we need to concentrate on these aspects:

  1. Traffic Types (e.g., Video, Voice): Mimic numerous traffic types that affect QoE.
  2. Performance Metrics (QoS): Assess network metrics such as throughput, latency, jitter, and packet loss.
  3. Mapping QoS to QoE: Translate QoS metrics into QoE, usually via mathematical models or thresholds according to user satisfaction.

Steps to Implement QoE in NS2

  1. Install NS2

Make sure we have NS2 installed on the computer.

  1. Create a TCL Script to Simulate Traffic with QoE Relevance

For QoE simulation, we need to create network traffic like video, voice, web browsing and establish network impairments such as packet loss, delay that impacts user experience.

The below is a sample of how to mimic traffic that would impact QoE:

Example: Video Streaming Simulation with QoE Measurement

# Define a simulator object

set ns [new Simulator]

# Define trace and nam files

set tracefile [open out.tr w]

set namfile [open out.nam w]

$ns trace-all $tracefile

$ns namtrace-all $namfile

# Define a ‘finish’ procedure to end simulation and process results

proc finish {} {

global ns tracefile namfile

$ns flush-trace

close $tracefile

close $namfile

exec nam out.nam &

exit 0

}

# Create nodes

set n0 [$ns node]

set n1 [$ns node]

set n2 [$ns node]

set n3 [$ns node]

# Create duplex links with different bandwidths and delays

$ns duplex-link $n0 $n1 2Mb 10ms DropTail

$ns duplex-link $n1 $n2 1Mb 50ms RED

$ns duplex-link $n2 $n3 1Mb 20ms DropTail

# Traffic simulation (Video Streaming with UDP)

set udp [new Agent/UDP]

set null [new Agent/Null]

$ns attach-agent $n0 $udp

$ns attach-agent $n3 $null

$ns connect $udp $null

# CBR traffic to simulate constant streaming flow

set cbr [new Application/Traffic/CBR]

$cbr attach-agent $udp

$cbr set packetSize_ 1000

$cbr set rate_ 1Mb  ;# Video rate of 1 Mbps

$cbr start 0.5

$cbr stop 4.0

# Simulate network impairments for QoE (Packet Loss, Delay)

$ns queue-limit $n1 $n2 10  ;# Queue limit to introduce congestion

$ns queue-limit $n2 $n3 5   ;# Reduce queue size to increase packet drops

# Schedule finish and run simulation

$ns at 5.0 “finish”

$ns run

  1. Explanation of the Script
  • Traffic (Video Streaming): UDP and CBR (Constant Bit Rate) traffic mimic a video stream with a 1 Mbps flow.
  • Network Links with Varying Bandwidths/Delays: Different link capacities and delays mimic to real-world conditions impacts the QoE.
  • Queue Management for Impairments: Queue limits and RED (Random Early Detection) on links mimic the network congestion and packet drops, that can effect video quality.
  • QoE-Affecting Metrics: Jitter, packet loss, delay, and throughput are parameters that will ultimately impact the perceived quality of the video.
  1. Mapping QoS Metrics to QoE

QoE is usually determined by translating QoS parameters like delay, jitter, and packet loss into user experience. Common approaches has contain:

  • Mean Opinion Score (MOS): A subjective QoE measure usually used for voice and video applications. MOS values range from 1 (bad) to 5 (excellent), and they can be calculated based on QoS metrics.
  • Video Quality Models: Tools such as PSNR (Peak Signal-to-Noise Ratio) or SSIM (Structural Similarity Index) are usually used to evaluate video quality according to packet loss and jitter.

We can generate the custom mapping according to study’s specific QoS thresholds.

  1. Measure QoS Parameters from the Simulation

Once the simulation is done, we will have the trace file (out.tr). Use this file to estimate the following QoS metrics:

  • Throughput: Total bytes received over time.
  • End-to-End Delay: The time taken for a packet to travel from source to destination.
  • Jitter: Variation in packet arrival times that impacts real-time traffic like video.
  • Packet Loss: The number of packets dropped because of network conditions.

We can extract these parameters using tools such as AWK or custom scripts to process the trace file. Here’s an sample of an AWK script to estimate throughput:

awk ‘{

if ($1 == “r” && $4 == “AGT” && $3 == “cbr”) {

sum += $6;

}

} END { print “Throughput: “, sum/5, “bits/sec”; }’ out.tr

  1. QoE Attainment Based on Metrics

To map these QoS metrics into QoE:

  • Latency and Delay: High delay cause to lower QoE, particularly for interactive services (e.g., video calls).
  • Packet Loss: Significant packet loss will reduce video/audio quality. Loss thresholds (e.g., 1-2% for video) can interpret into MOS scores.
  • Jitter: Excessive jitter can leads buffering issues, minimizing the user’s experience in streaming services.

Example QoE Mapping Table

QoS Metric Acceptable Range QoE Effect (MOS Score)
Packet Loss < 1% 4.5-5 (Excellent)
Delay (Latency) < 150ms 4-5 (Very Good)
Jitter < 30ms 4-5 (Very Good)
Throughput > Required rate (1 Mbps) 5 (Excellent)
High Packet Loss > 5% 1-2 (Poor)
High Jitter > 100ms 1-2 (Poor)
High Delay > 250ms 1-3 (Fair to Poor)
  1. Visualize the Simulation

To envision the network and see how the traffic flows among the nodes:

  • Execute nam to open the NAM animation file:

nam out.nam

  1. Analyse the Results

Once the simulation is completed, we can:

  • Evaluate the trace file to extract QoS metrics.
  • Map the extracted QoS parameter to QoE using a subjective model or tool such as MOS, PSNR, or SSIM.
  • We also compose scripts to estimate average delay, packet loss, throughput, and jitter to relate them with QoE.

Conclusion

Executing QoE attainment in NS2 involves:

  1. Traffic simulation (e.g., video, voice) and establishing network impairments like packet loss, delay.
  2. Measurement of QoS parameters such as delay, jitter, packet loss, and throughput.
  3. Mapping QoS metrics to QoE, using models like MOS or PSNR to measure the user’s experience.

The above techniques were helpful to enforce the network QoE attainment in ns2 tool that has to generate the network traffic then apply the network impairment to execute the outcomes using ns2 tool. We plan to provide more information regarding the network QoE attainment.

By executing QoE, we provide optimal results. We guide you with implementation support of Network QoE Attainment using the NS2 tool, we will stand by you as your reliable partner in each and every step.