INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
Enhancing Social Experience in Smart Stadium with Wi-Fi 6 Quality  
Management  
Nurul Azma Zakaria1*, Muhammad Harith Hakim Rosman 1, Fairul Azni Jafar2, Erman Hamid1, Wan  
Faezah Abbas3, Muhammad Rahmatur Rahman Mohamad Nazir4  
1Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,  
76100 Durian Tunggal, Melaka, Malaysia  
2Fakulti Teknologi dan Kejuruteraan Industri dan Pembuatan, Universiti Teknikal Malaysia Melaka,  
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia  
3Fakulti Sains Komputer & Matematik, Universiti Teknologi MARA, 40450 Shah Alam, Selangor,  
Malaysia  
4E-Content (M) Sdn. Bhd., Suite 3A-1, Block 4805 CBD Perdana, 2, Jln Perdana, Cyber 12, 63000  
Cyberjaya, Selangor, Malaysia  
*Corresponding Author  
DOI: https://doi.org/10.47772/IJRISS.2025.91200056  
Received: 09 December 2025; Accepted: 16 December 2025; Published: 31 December 2025  
ABSTRACT  
Smart stadiums are revolutionizing live sports by creating digitally connected environments that turn spectators  
into active participants. However, when thousands of fans simultaneously use public Wi-Fi for streaming and  
sharing, network congestion often occurs. This overload degrades connectivity and disrupts live broadcasts for  
remote viewers, causing buffering, poor quality, and interruptions. Limited bandwidth and diverse user demands  
further challenge Wi-Fi performance, leading to inconsistent experiences. This study proposes a Wi-Fi 6-based  
Quality of Service (QoS) management framework to ensure uninterrupted, high-quality video streaming during  
live events. Developed using Python and NS-3 simulation tools, the framework employs the Priority Queuing  
algorithm to optimize traffic flow. Agile methodology guided iterative development for scalability and  
adaptability. Performance was evaluated under simulated high-density conditions using key QoS metrics:  
throughput, packet loss ratio, traffic volume, and bandwidth usage. Results show that Priority Queuing  
significantly reduces congestion, improves responsiveness, and supports real-time traffic optimization. the study  
highlights how reliable Wi-Fi 6 connectivity can enhance inclusivity, improve operational efficiency, and foster  
more immersive and equitable social experiences. Future work will explore advanced prioritization techniques,  
integration with emerging technologies, and personalization based on user feedback, which bridges technical  
precision with meaningful social impact in the Wi-Fi 6 era.  
Keywords: Smart Stadiums, Wi-Fi 6 Technology, Priority Queuing, Traffic Prioritization, QoS Management  
INTRODUCTION  
The evolution of smart stadiums marks a significant transformation in how modern societies experience live  
sports and large-scale events. Unlike traditional venues, smart stadiums integrate advanced digital infrastructure,  
including Wi-Fi 6 networks, Internet of Things (IoT) sensors, and real-time data analytics, to enhance audience  
engagement and operational efficiency. Today’s spectators are no longer passive observers; they actively  
participate by streaming replays, sharing content on social media, and interacting through mobile applications.  
This digitally mediated participation contributes to what scholars describe as the social experience: a blend of  
physical presence and digital interaction that shapes the atmosphere and communal value of contemporary  
events.  
Page 642  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
Connectivity is now central to this collective experience. When Wi-Fi networks become congested or unstable,  
audience engagement suffers, and the perceived quality of the event declines. Ensuring reliable connectivity is  
therefore not just a technical concern, but it is a matter of social equity and public satisfaction.  
The concept of social experience extends beyond technical metrics to encompass how individuals perceive,  
communicate, and emotionally engage within connected public spaces. In smart stadiums, this includes sharing  
multimedia in real time, accessing personalized content, and participating in digital communities. High-quality  
wireless connectivity enables these interactions, fostering inclusivity and cohesion. Conversely, disruptions in  
connectivity fragment social unity and diminish immersion. Thus, wireless quality management directly  
influences how audiences connect, express, and co-experience events.  
As integral components of the broader smart-city ecosystem, smart stadiums function as digitally enhanced  
venues that not only deliver immersive spectator experiences but also optimize crowd flow, energy consumption,  
and operational efficiency. By merging advanced connectivity with human-centered design, these environments  
exemplify the convergence of technology and social innovation.  
To address the growing demand for reliable connectivity in these environments, this study proposes a Wi-Fi 6-  
based adaptive quality management framework that ensures stable network performance in smart stadiums. The  
framework is developed using Python and NS-3 simulation tools and incorporates the Priority Queuing (PQ)  
algorithm to manage network traffic efficiently. The system is evaluated based on key Quality of Service (QoS)  
parameters such as throughput, packet loss ratio, network traffic volume, and bandwidth usage to assess its  
effectiveness in minimizing latency, reducing packet loss, and maintaining seamless streaming under high-  
density conditions.  
The paper is structured as follows: Section 2 begins with a review of related work that forms the foundation of  
this study. Following that, Section 3 outlines the methodology and simulation framework, detailing the technical  
approach and implementation process. Section 4 then presents the performance results, highlighting the  
outcomes of the simulation and analysis based on key evaluation metrics. Section 5 then discusses the findings  
in relation to the study’s objectives and observed outcomes. Lastly, Section 6 concludes the paper by  
summarizing its significance, highlighting the main contributions, and suggesting directions for future research.  
LITERATURE REVIEW  
Wi-Fi 6 (IEEE 802.11ax) introduces several advanced features that significantly improve throughput, efficiency,  
and reliability in high-density environments. A study by [1] demonstrates that Wi-Fi 6 can support up to four  
times more concurrent users than Wi-Fi 5, while maintaining lower latency. However, these benefits are highly  
dependent on effective QoS control and real-time traffic prioritization. Without such mechanisms, network  
contention can still lead to performance degradation, especially in environments like smart stadiums where  
thousands of users compete for bandwidth simultaneously.  
QoS mechanisms are essential for ensuring that critical data, such as live video streams, receive transmission  
priority over less time-sensitive traffic. Traditional models like Weighted Fair Queuing and Enhanced  
Distributed Channel Access (EDCA) have been widely implemented in wireless networks, but they often rely  
on static configurations that struggle to adapt to dynamic network conditions. Research in [2-8] advocates for  
adaptive QoS models that adjust priorities based on real-time network load and application requirements,  
offering more responsive and scalable solutions.  
Smart stadiums, as part of the broader smart-city ecosystem, integrate digital technologies not only for  
connectivity but also for crowd behavior management, energy efficiency, and personalized user experiences [11-  
17]. From a social innovation perspective, these venues foster inclusive and interactive environments that  
enhance fan engagement, emphasizing the role of connectivity as a public good, where equitable digital access  
contributes to shared cultural and emotional experiences.  
Several studies have explored QoS management strategies to improve the quality of sports broadcasting and  
multimedia streaming. Shabrina et al. in [18] investigated the use of Content Delivery Networks (CDNs) for  
Page 643  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
HTTP Live Streaming (HLS) in Indonesia, focusing on affordability and performance for small and medium-  
sized enterprises. Meanwhile, [2] introduced an adaptive framework called ACCeSS to enhance live streaming  
performance, particularly in virtual and augmented reality applications. Work in [3] proposed adaptive error  
protection for video transmission over WLANs, while Bose and Sujatha in [19] developed a fuzzy logic-based  
QoS monitoring system for cloud-based media services.  
While these studies offer valuable insights, a critical comparison reveals several gaps. Most prior works focus  
on isolated aspects of QoS, such as CDN deployment, fuzzy logic monitoring, or specific adaptive frameworks,  
without integrating multiple performance dimensions into a unified solution. Few studies address real-time  
traffic prioritization, dynamic resource allocation, and scalability in high-density environments simultaneously.  
Moreover, many models rely on static configurations or are tailored to specific use cases like VR/AR or cloud  
services, limiting their applicability to live sports broadcasting in smart stadiums.  
The proposed study addresses these gaps by introducing a comprehensive Wi-Fi 6-based QoS management  
framework that integrates Priority Queuing [20], real-time monitoring, and adaptive bandwidth allocation.  
Unlike previous works, it is specifically designed for high-density environments and evaluated using key QoS  
parameters, i.e, throughput, packet loss ratio, network traffic volume, and bandwidth usage under simulated  
stadium conditions. This approach not only enhances technical performance but also supports social innovation  
by promoting digital inclusivity and immersive audience engagement.  
METHODOLOGY  
Research Design  
Agile methodology, as represented in Fig. 1, shows a contemporary approach to software development that  
follows an iterative and incremental process, consistent with Software Development Life Cycle (SDLC)  
principles. It emphasizes collaboration, adaptability, and customer satisfaction, enabling rapid and responsive  
development [21].  
This methodology suits the study due to its ability to accommodate the dynamic and evolving nature of enhancing  
sports broadcasting quality through Wi-Fi 6-based QoS management. Unlike traditional models such as the  
waterfall approach, which follow a linear and sequential SDLC structure, Agile supports continuous feedback  
and iterative refinement. This flexibility allows the study to respond effectively to changing requirements while  
maintaining a focus on delivering high-quality software.  
Fig. 1 Agile Methodology [21]  
1)  
Iteration Backlog: The iteration backlog serves as a structured task list that defines the features and  
requirements to be implemented during each development phase. It helps prioritize tasks and ensures systematic  
Page 644  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
progress, particularly in addressing real-time monitoring, traffic prioritization, and bandwidth management.  
2)  
Requirement: The development process begins with a clear understanding of the study requirements.  
Emphasis is placed on identifying functional requirements related to QoS management, including real-time  
monitoring, traffic prioritization, and bandwidth optimization. These requirements guide the development and  
ensure alignment with the study’s objectives.  
3)  
Verification: Verification involves continuous testing and validation to ensure the software meets quality  
standards. This stage helps identify and resolve issues early, contributing to a reliable and robust QoS  
management solution.  
4)  
Implementation: Implementation proceeds incrementally, with each iteration involving the coding, design,  
and integration of components necessary to achieve the goals. Python is used as the primary programming  
language, and best practices are followed to ensure structured and maintainable development.  
5)  
Product Owner Review: These reviews assess the software’s compliance with defined requirements and  
objectives, allowing for timely adjustments and improvements.  
6)  
Final Product: The final product emerges through successive iterations, each contributing to the overall  
functionality and performance of the QoS management software. The result is a comprehensive solution that  
optimizes network performance and enhances the streaming experience.  
System Design  
With the requirements established, Fig. 2 illustrates the system design flow of the high-level architecture of the  
web-based application. The architecture includes both the backend and frontend components. The backend,  
developed using Python, handles the implementation of traffic prioritization, real-time monitoring, and  
bandwidth management functionalities. The frontend, created using HTML, CSS, and JavaScript, provides the  
user interface for interacting with the system.  
Fig. 1 System Design Flow Chart  
Simulation Design  
Fig. 3 shows that 50 nodes were created in NS-3 to simulate a realistic stadium environment. The red dots  
represent the nodes in the wireless network. Node 50 is the access point, while the other remaining nodes are the  
mobile stations. The grid lines do not represent the distance between nodes; instead, the grid lines are there to  
aid the visualization and understanding of the network layout.  
Page 645  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
Fig. 3 Wireless Network Topology in NS-3  
RESULTS AND ANALYSIS  
Coding and Scripting  
Python Implementation  
The Python program was structured into modular scripts to manage different functionalities:  
1. main.py – Controls the overall program flow and integrates all components.  
2. real_time_monitor.py – Captures and monitors packets in real time for traffic analysis.  
3. traffic_prioritization.py – Implements the Priority Queuing algorithm for traffic optimization.  
4. bandwidth_manager.py – Allocates bandwidth dynamically and displays usage for the target address.  
This Python-based QoS framework was later integrated into an NS-3 simulation to evaluate performance under  
realistic stadium conditions.  
NS-3 Simulation  
Two simulation scenarios were developed:  
1. Baseline Scenario (Without QoS)  
The baseline scenario represents a stadium wireless network topology consisting of 50 mobile stations connected  
to a single access point. To replicate real-world conditions, high traffic congestion was introduced within the  
network environment. In this setup, no QoS algorithm was applied, allowing the network to operate under default  
conditions without prioritization or traffic management mechanisms.  
2. Enhanced Scenario (With Priority Queuing)  
The enhanced scenario maintains the same stadium wireless network topology and traffic conditions as the  
baseline configuration, with 50 mobile stations connected to a single access point under high congestion.  
However, in this setup, Priority Queuing is implemented at the access point to optimize traffic flow. This  
mechanism ensures that higher-priority packets are transmitted first, reducing delays for critical data and  
improving overall network performance compared to the baseline scenario.  
Page 646  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
Test Result  
QoS Algorithm Preliminary Evaluation in NS-3  
To identify the most suitable QoS algorithm for the simulated stadium environment, several algorithms were  
tested in NS-3. The goal was to determine compatibility with wireless network topology and assess feasibility  
for implementation. The algorithms evaluated include:  
1. Weighted Fair Queuing (WFQ) – Ensures fair bandwidth allocation by assigning weights to flows; higher-  
weight flows receive proportionally more bandwidth.  
2. Token-Bucket Filtering – Shapes traffic by regulating packet transmission rates using a token bucket  
mechanism.  
3. First-In-First-Out (FIFO) – Processes packets in the order of arrival without prioritization.  
4. Priority Queuing (PQ) – Assigns priority levels to packets; higher-priority packets are transmitted first.  
5. Round-Robin – Allocates resources cyclically, ensuring equal distribution among flows.  
Table I Algorithm preliminary test results  
QoS algorithm  
Supported in NS-3  
Network Topology  
n/a  
Weighted-fair queueing  
Token-bucket filtering  
First-in-first-out (FIFO)  
Priority queuing (PQ)  
Random Early Detection  
Round-Robin  
No  
Yes  
Yes  
Yes  
Yes  
No  
Point-to-point  
Point-to-point  
Wireless  
Dumbbell  
n/a  
The results indicate that most algorithms are either unsupported in NS-3 or limited to point-to-point topologies,  
making them unsuitable for wireless stadium environments. Priority Queuing emerged as the most appropriate  
choice, as it is fully supported in NS-3 and can be implemented within a wireless network topology. It meets all  
requirements for this study, enabling effective traffic prioritization under high-density conditions.  
Python System Testing and Results  
System testing represents a critical phase in validating the Python-based QoS management system. This process  
ensures that the system meets functional requirements and delivers a user-friendly interface. Comprehensive  
testing helps identify and resolve potential bugs, usability issues, and performance gaps, thereby improving  
overall reliability and user experience. Ultimately, successful testing confirms compliance with project  
objectives and supports the core goal of providing an effective and intuitive QoS management solution.  
Testing Procedure  
The following steps outline the system testing workflow:  
1. Program Initialization  
o
Execute the main Python program via the command prompt and select the desired option.  
www.rsisinternational.org  
Page 647  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
2. Real-Time Monitoring  
o
o
Enter the target IP address.  
The system displays real-time packet flow details.  
3. Bandwidth Management  
o Select the option to manage bandwidth.  
. Allocate Bandwidth: Specify the target address and packet details.  
.
.
Adjust Bandwidth: Provide the target address and new bandwidth value.  
Display Bandwidth: View allocated bandwidth for a specific address.  
4. Traffic Prioritization  
Select the option to access the PQ feature.  
o
. Add Packets: Input packet details, and assign priority (lower number = higher priority).  
. Process Queue: Perform repeatedly until all packets are processed.  
o
The system confirms completion with the message “Queue is empty.”  
NS-3 Simulation Testing and Results  
The objective of NS-3 simulation testing was to evaluate the impact of QoS algorithms in a congested wireless  
network environment. By simulating high-density traffic conditions, the tests provided insights into the  
effectiveness of QoS strategies in managing congestion, ensuring timely data delivery, and maintaining service  
quality. These results guide the selection and optimization of QoS mechanisms for robust and reliable network  
performance.  
Testing Procedure  
1. Baseline Simulation (Without QoS)  
The baseline simulation begins by executing the first NS-3 script, which models a stadium wireless network  
without applying any QoS algorithm. This configuration allows the network to operate under default conditions,  
providing a reference point for performance evaluation. During the simulation, key performance metrics such as  
throughput, packet loss ratio, network traffic volume, and bandwidth usage are recorded. These metrics serve as  
the foundation for later analysis and comparison with enhanced scenarios.  
2. Enhanced Simulation (With Priority Queuing)  
The enhanced simulation involves running the second NS-3 script, which incorporates the Priority Queuing  
algorithm at the access point. This approach introduces traffic prioritization to improve network performance  
under high congestion conditions. Similar to the baseline simulation, the enhanced setup generates key  
performance metrics, including throughput, packet loss ratio, network traffic volume, and bandwidth usage.  
These results are collected for comparative evaluation against the baseline scenario to assess the impact of  
implementing Priority Queuing.  
The baseline scenario exhibited significant congestion, resulting in reduced throughput and higher packet loss.  
In contrast, the Priority Queuing implementation improved traffic flow, minimized packet loss, and optimized  
bandwidth utilization under identical conditions. These findings confirm Priority Queuing as an effective QoS  
strategy for high-density wireless environments.  
Page 648  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
NS-3 Test Result Analysis  
To facilitate comparison, the simulation results were visualized in graph form. In NS-3, flows represent  
unidirectional data transfers between a source and destination node, serving as logical channels for packet  
transmission. The analysis focused on key performance metrics: throughput, packet loss ratio, and network  
traffic volume.  
Throughput  
Fig. 4 illustrates the throughput comparison between the baseline scenario (No Algorithm) and the enhanced  
scenario (Priority Queuing) across three flows. In the No Algorithm case, throughput values are extremely  
low, with Flow 1 and Flow 3 registering near zero and Flow 2 achieving only about 0.035 Mbps. These  
negative or negligible values indicate severe congestion and the inability of the network to deliver packets  
effectively under high traffic conditions.  
Conversely, the Priority Queuing scenario demonstrates a significant improvement in throughput. Flow 1  
achieves approximately 0.085 Mbps, which is more than double the highest value recorded in the baseline  
scenario. Flow 2 also shows improvement, reaching around 0.018 Mbps, while Flow 3 remains very minimal.  
This distribution reflects the effect of prioritization, where higher-priority flows receive more bandwidth,  
ensuring critical data is transmitted successfully even during congestion.  
Overall, the graph confirms that Priority Queuing not only enables successful packet delivery but also  
optimizes throughput for prioritized flows, making it a robust solution for congested wireless environments.  
Fig. 4 NS-3 Throughput Comparison Graph  
Packet Loss Ratio  
Fig. 5 presents the packet loss ratio comparison between the baseline scenario (No Algorithm) and the  
enhanced scenario (Priority Queuing) across three flows. In the No Algorithm configuration, all flows  
exhibit a packet loss ratio of 1.0 (100%), meaning every transmitted packet was lost due to severe  
congestion and lack of traffic management. This indicates that the network was unable to deliver any  
data successfully under high-load conditions.  
In contrast, the Priority Queuing scenario shows a packet loss ratio of 0 for all flows, signifying complete  
packet delivery without any loss. This dramatic improvement highlights the effectiveness of Priority  
Queuing in managing congestion and ensuring reliable data transmission. By prioritizing critical packets,  
the algorithm eliminates packet drops, which is essential for maintaining service quality in dense wireless  
environments.  
The graph clearly demonstrates that Priority Queuing transforms network performance from total failure  
to full reliability, making it a superior solution for scenarios with heavy traffic.  
Page 649  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
Fig. 5 NS-3 3 Packet Loss Ratio Comparison Graph  
Network Traffic Volume  
Fig. 6 compares the network traffic volume between the baseline scenario (No Algorithm) and the enhanced  
scenario (Priority Queuing) across three flows. In the No Algorithm configuration, traffic volume is  
extremely low, with Flow 1 and Flow 2 showing negligible values and Flow 3 reaching only about 20,000  
bytes. This indicates that most packets were dropped, and very little data reached the destination under  
congested conditions.  
In contrast, the Priority Queuing scenario demonstrates a substantial improvement. Flow 1 achieves  
approximately 85,000 bytes, which is more than four times higher than the highest value in the baseline  
scenario. Flow 2 also shows improvement, reaching around 15,000 bytes, while Flow 3 remains minimal  
but positive. This distribution reflects the prioritization mechanism, where higher-priority flows receive  
more bandwidth and transmit significantly more data.  
The graph clearly illustrates that Priority Queuing enables actual data transmission under heavy congestion,  
ensuring that critical flows maintain service quality while reducing packet loss and improving overall  
network efficiency.  
Fig. 6 NS-3 3 Network Traffic Volume Comparison Graph  
The testing phase, conducted in NS-3 after validating the Python QoS system, demonstrates that Priority Queuing  
(PQ) is highly effective in congested wireless environments. PQ significantly reduces packet loss, ensures  
successful data transmission, and improves resource utilization compared to scenarios without QoS. These  
findings validate PQ as the optimal algorithm for this study.  
DISCUSSION  
The findings of this study demonstrate the technical feasibility and practical value of an adaptive Wi-Fi 6 quality  
management framework in smart stadium environments. By implementing real-time bandwidth allocation and  
Page 650  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
traffic prioritization, the system effectively mitigates service degradation under high-density conditions. The  
integration of a Python-based control mechanism further enhances scalability and supports flexible deployment  
in real-world scenarios, making it a viable solution for large public venues.  
Beyond its technical contributions, the study highlights the broader social and managerial implications of reliable  
wireless connectivity. In digitally connected public spaces, network performance directly influences digital  
inclusion. When connectivity is stable and equitable, all attendees can access digital services such as live  
streaming, social media sharing, and interactive applications without disruption. This fosters a more inclusive  
and participatory environment, where spectators are not only consumers of content but active contributors to the  
event experience. From a management perspective, the framework introduces a new dimension of operational  
innovation. By leveraging live network data, venue operators can make informed decisions about infrastructure,  
crowd flow, and safety communications, treating connectivity as a strategic asset rather than a background utility.  
Smart stadiums, in this context, evolve into spaces of technological citizenship where equitable access to  
information and digital experiences contributes to a shared sense of community. This study illustrates how Wi-  
Fi 6 quality management supports digital inclusion, which in turn enhances the social experience of live events.  
CONCLUSION  
This study developed and evaluated an adaptive Wi-Fi 6 quality management framework aimed at improving  
both broadcasting performance and the overall social experience in smart stadium environments. Through  
simulation and analysis, the system demonstrated its ability to reduce latency and packet loss, maintain stable  
throughput under high-density conditions, and deliver smoother live streaming. These technical improvements  
contribute to a more immersive and satisfying experience for spectators, reinforcing the importance of reliable  
connectivity in shaping collective engagement. The findings underscore the idea that connectivity is not merely  
a technical utility but a form of social infrastructure. In public venues like smart stadiums, dependable wireless  
service enables inclusive participation, real-time interaction, and audience satisfaction. As such, Wi-Fi 6 quality  
management is positioned not only as an engineering achievement but also as a social innovation that supports  
equitable access and digital transformation.  
This research contributes to the field by introducing a technically advanced framework for adaptive QoS  
management in Wi-Fi 6 networks, validated through NS-3 simulations. It also offers a conceptual perspective  
that integrates digital inclusion and social experience into the analysis of wireless systems, presenting a new way  
to interpret connectivity in public event settings. Looking ahead, future research may explore the integration of  
machine learning for predictive congestion control, the orchestration of hybrid 5G Wi-Fi networks, and the  
inclusion of user-centered methods such as ethnographic studies or satisfaction surveys. These interdisciplinary  
extensions will further align technical system design with meaningful social outcomes. In an era where human  
connection increasingly depends on digital infrastructure, managing network quality becomes an act of social  
design. Smart stadiums exemplify how technological excellence, when guided by social purpose, can transform  
collective experiences into shared innovation.  
ACKNOWLEDGMENT  
The authors extend sincere appreciation to the Universiti Teknikal Malaysia Melaka (UTeM) and the Fakulti  
Teknologi Maklumat dan Komunikasi (FTMK) for the valuable support provided, including financial assistance  
and access to essential resources, which facilitated the successful execution of this study. Gratitude is also  
expressed to all individuals and organizations whose contributions, collaboration, and encouragement  
significantly supported the completion of this research.  
REFERENCES  
1. Mozaffariahrar, E., Theoleyre, F., & Menth, M. (2022). A survey of Wi-Fi 6: Technologies, advances,  
and challenges. Future Internet, 14(10), 293.  
2. Ji, X., Han, B., Xu, C., Song, C., Su, J. (2023). Adaptive QoS-aware multipath congestion control for  
live streaming. Computer Networks, 220, 109470. https://doi.org/10.1016/j.comnet.2022.109470  
Page 651  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
3. Li, Q., vanderSchaar, M. (2004). Providing adaptive QoS to layered video over wireless local area  
networks through real-time retry limit adaptation. IEEE Transactions on Multimedia, 6(2), 278–290.  
https://doi.org/10.1109/tmm.2003.822792  
4. Huang, C. J., Hu, K. W., & Cheng, H. W. (2023). An adaptive bandwidth management algorithm for  
next-generation vehicular networks. Sensors, 23(18), 7767.  
5. Swain, S. K., & Nanda, P. K. (2021). Adaptive queue management and traffic class priority based fairness  
rate control in wireless sensor networks. IEEE Access, 9, 112607-112623.  
6. Shao, Y. (2025, June). Research on MAC and Routing Protocols for Stadiums Based on Optimisation  
Algorithms. In 2025 5th International Conference on Artificial Intelligence, Big Data and Algorithms  
(CAIBDA) (pp. 432-435). IEEE.  
7. Beshley, M., Kryvinska, N., Seliuchenko, M., Beshley, H., Shakshuki, E. M., & Yasar, A. U. H. (2020).  
End-to-End QoS “smart queue” management algorithms and traffic prioritization mechanisms for  
narrow-band internet of things services in 4G/5G networks. Sensors, 20(8), 2324.  
8. Fang, L. (2021). Intelligent Scheduling Method Supporting Stadium Sharing. Discrete Dynamics in  
Nature and Society, 2021(1), 3143434.  
9. Chen, J., & Zhang, D. (2025). Design of IoT-Based Crowd Flow Monitoring System for Smart Sports  
Venues. In Intelligent Transportation and Smart Cities (pp. 13-22). IOS Press.  
10. Mohammadi, S., Ghaffarisadr, S. I., & Dos Santos, M. A. Artificial Intelligence (AI): A New Window  
to Smart Stadiums. In Digital Transformation in Sports (pp. 87-109). Auerbach Publications.  
11. Şimşek, A., & Devecioğlu, S. (2025). The Future of Technology at Mega Sports Events. Türkiye Spor  
Bilimleri Dergisi, 9(1), 48-66.  
12. Schut, P. O., & Glebova, E. (2022). Sports spectating in connected stadiums: mobile application Roland  
Garros 2018. Frontiers in sports and active living, 4, 802852.  
13. Bahbahani, M. S., & Alsusa, E. (2019). A game theoretic framework for quality of experience  
enhancement in dense stadia. IEEE Access, 7, 102606-102616.  
14. Bajpai A, Bagchi A (2020): Enhancement of fans experience in stadium through better facility  
management,  
Ann  
Trop  
Med  
&
Public  
Health;  
23(S17):  
SP231702.  
DOI:  
http://doi.org/10.36295/ASRO.2020.231702  
15. Raman, R., & Singh, A. (2023, November). 5G and IoT for smart stadium operations for enhancing fan  
experience and safety. In 2023 International Conference on Advances in Computation, Communication  
and Information Technology (ICAICCIT) (pp. 1128-1132). IEEE.  
16. Lusweti, S. W., & Odawa, J. (2023). Towards the advanced technology of smart, secure and mobile  
stadiums: a perspective of fifa world Cup Qatar 2022. Computer Science and Information  
Technology, 11(2), 21-30.  
17. Levallet, N., O’Reilly, N., Wanless, E., Naraine, M., Alkon, E., & Longmire, W. (2019). Enhancing the  
fan experience at live sporting events: The case of stadium Wi-Fi. Case Studies in Sport  
Management, 8(1), 6-12.  
18. Shabrina, W. E., Wisaksono Sudiharto, D., Ariyanto, E., Makky, M. A. (2020a). The QoS improvement  
using CDN for live video streaming with HLS. 2020 International Conference on Smart Technology and  
Applications (ICoSTA).  
19. Bose, S. B., Sujatha, S. S. (2020). Fuzzy logic based QoS management and monitoring system for cloud  
computing. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS).  
https://doi.org/10.1109/iciss49785.2020.9315874  
20. Wu, G. (2025). An innovative priority queueing strategy for mitigating traffic congestion in complex  
networks. Mathematics, 13(3), 495.  
21. Sinha, A., Das, P. (2021). Agile methodology vs. traditional waterfall SDLC: A case study on quality  
assurance process in software industry. 2021 5th International Conference on Electronics, Materials  
Engineering  
&
amp;  
amp;  
Nano-Technology  
(IEMEN  
Tech).  
https://doi.org/10.1109/iementech53263.2021.9614779  
Page 652  
www.rsisinternational.org