TY - GEN
T1 - Quality of Experience Prediction for First-Person Shooter Online Gaming
T2 - 23rd IEEE Consumer Communications and Networking Conference, CCNC 2026
AU - Zion, Yehonatan
AU - Paz, Eyal
AU - Dubin, Ran
AU - Dvir, Amit
AU - Hajaj, Chen
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - Latency is the most impactful on fairness and Quality of Experience (QoE) in First-Person Shooter (FPS) games. High latency degrades the QoE of players, who may leave the game if unsatisfied with their QoE. Modern FPS games make great efforts to maintain an excellent QoE even under a poor Internet connection with high latency. Those efforts include the wide distribution of game servers and many software optimizations to smooth the effect of lags in the games. This study aims to provide insights into QoE estimation for network-intensive applications by examining one of the most prominent FPS games of the past two decades: Call of Duty. We observed that the game dynamically adjusts its network traffic behavior, including packet size and transmission rate, in response to variations in network quality. However, the ISP does not have this capability since the network traffic is encrypted; observing the game's network traffic does not expose its nature and most certainly does not expose the game player's intensity, latency, or QoE. We propose a novel technique for estimating latency and QoE in FPS games from an ISP-level perspective. In our evaluation, the model detected problematic latency in near real-time with 81% accuracy and an 80% F1 on a 10-second window, highlighting a trade-off between responsiveness and predictive performance. The dataset generated for this study is publicly available to support further research.
AB - Latency is the most impactful on fairness and Quality of Experience (QoE) in First-Person Shooter (FPS) games. High latency degrades the QoE of players, who may leave the game if unsatisfied with their QoE. Modern FPS games make great efforts to maintain an excellent QoE even under a poor Internet connection with high latency. Those efforts include the wide distribution of game servers and many software optimizations to smooth the effect of lags in the games. This study aims to provide insights into QoE estimation for network-intensive applications by examining one of the most prominent FPS games of the past two decades: Call of Duty. We observed that the game dynamically adjusts its network traffic behavior, including packet size and transmission rate, in response to variations in network quality. However, the ISP does not have this capability since the network traffic is encrypted; observing the game's network traffic does not expose its nature and most certainly does not expose the game player's intensity, latency, or QoE. We propose a novel technique for estimating latency and QoE in FPS games from an ISP-level perspective. In our evaluation, the model detected problematic latency in near real-time with 81% accuracy and an 80% F1 on a 10-second window, highlighting a trade-off between responsiveness and predictive performance. The dataset generated for this study is publicly available to support further research.
KW - Call of Duty
KW - First-Person Shooter
KW - Latency
KW - Network Traffic Analysis
KW - Online Games
KW - Quality of Experience
UR - https://www.scopus.com/pages/publications/105034210007
U2 - 10.1109/CCNC65079.2026.11366349
DO - 10.1109/CCNC65079.2026.11366349
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AN - SCOPUS:105034210007
T3 - Proceedings - IEEE Consumer Communications and Networking Conference, CCNC
BT - 2026 IEEE 23rd Consumer Communications and Networking Conference, CCNC 2026
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 January 2026 through 12 January 2026
ER -