QCI Optimization to Minimize Latency and Enhance User Experience
Main Article Content
Keywords
QoS, latency, throughput, DRX, Pre-allocation, PDCP, scheduling
Abstract
Limited QCIs (QoS Class Identifiers) restrict the handling different service types with varying quality requirements. This necessitates research on QoS management to minimize latency and improve user experience, particularly for real-time applications like video conferencing and online gaming. This paper proposes a combined optimization scheme targeting QCI 3 to reduce latency. The approach involves disabling DRX, optimizing pre-allocation, and reducing the PDCP discard timer. The optimization performance is studied by taking the case of an e-sport game that demands low network latency, affecting the quality of the players' experience. The optimization scheme was validated through functionality, resource allocation, and air interface latency tests conducted under actual e-sport gaming conditions. Network latency was measured every minute to evaluate the impact of optimization on esports games running under QCI 7, QCI 3, and optimized QCI 3. In addition, air interface latency for optimized QCI 3 under networks with poor coverage and very high-capacity networks was compared to latency under QCI 8 (basic), QCI 7, and regular QCI 3. The optimization strategy demonstrated a significant reduction in air interface latency, up to 19% improvement compared to non-optimized QCI 3. It has reduced air interface latency's maximum, minimum, and standard deviation values during gameplay. The strategy also ensured concurrent operation with multiple QCI values without compromising other application’s throughput. The proposed optimization strategy effectively enhances the user experience by significantly reducing average latency and jitter.
References
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[17] S. Khwandah, B. Mohamad, J. Cosmas, P. Lazaridis, and Z. Zaharis, “Video performance using adaptive DRX switching for LTE-A,” in IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Jul. 2017, p. (pp. 1-5). doi: 10.1109/BMSB.2017.7986128.
[18] J. M. Liang, J. J. Chen, H. H. Cheng, and Y. C. Tseng, “An energy-efficient sleep scheduling with QoS consideration in 3GPP LTE-advanced networks for internet of things,” IEEE J. Emerg. Sel. Top. Circuits Syst., vol. 3, no. 1, pp. 13–22, 2013, doi: 10.1109/JETCAS.2013.2243631.
[19] J. M. Liang, J. J. Chen, P. C. Hsieh, and Y. C. Tseng, “Two-Phase Multicast DRX Scheduling for 3GPP LTE-Advanced Networks,” IEEE Trans. Mob. Comput., vol. 15, no. 7, pp. 1839–1849, Jul. 2016, doi: 10.1109/TMC.2015.2480090.
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[26] U. Toseef, T. Weerawardane, A. Timm-Giel, and C. Görg, “LTE system performance optimization by discard timer based PDCP buffer management,” in 8th International Conference on High-capacity Optical Networks and Emerging Technologies, Dec. 2011, p. (pp. 116-121). doi: 10.1109/HONET.2011.6149800.
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[28] A. M. Ahmed, A. Patel, and M. Z. A. Khan, “Reliability Enhancement by PDCP Duplication and Combining for Next Generation Networks,” in 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Apr. 2021, p. (pp.1-5). doi: 10.1109/VTC2021-Spring51267.2021.9449079.
[29] J. Sun, S. Zhang, S. Xu, and S. Cao, “High Throughput and Low Complexity Traffic Splitting Mechanism for 5G Non-Stand Alone Dual Connectivity Transmission,” IEEE Access, vol. 9, pp. 65162–65172, 2021, doi: 10.1109/ACCESS.2021.3076301.
[30] M. Finsterbusch, C. Richter, E. Rocha, J.-A. Muller, and K. Hanssgen, “A Survey of Payload-Based Traffic Classification Approaches,” IEEE Commun. Surv. Tutorials, vol. 16, no. 2, pp. 1135–1156, 2014, doi: 10.1109/SURV.2013.100613.00161.
[31] A. Azab, M. Khasawneh, S. Alrabaee, K.-K. R. Choo, and M. Sarsour, “Network traffic classification: Techniques, datasets, and challenges,” Digit. Commun. Networks, 2022, doi: https://doi.org/10.1016/j.dcan.2022.09.009.
[32] M. Seufert, S. Wassermann, and P. Casas, “Considering User Behavior in the Quality of Experience Cycle: Towards Proactive QoE-Aware Traffic Management,” IEEE Commun. Lett., vol. 23, no. 7, pp. 1145–1148, 2019, doi: 10.1109/LCOMM.2019.2914038.
[33] F. Metzger et al., “An Introduction to Online Video Game QoS and QoE Influencing Factors,” IEEE Commun. Surv. Tutorials, vol. 24, no. 3, pp. 1894–1925, Sep. 2022, doi: 10.1109/COMST.2022.3177251.
[34] S. Flinck Lindstrom, M. Wetterberg, and N. Carlsson, “Cloud Gaming: A QoE Study of Fast-paced Single-player and Multiplayer Gaming,” in 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), Dec. 2020, p. (pp. 34-45). doi: 10.1109/UCC48980.2020.00023.
[35] Y. Lin and H. Shen, “CloudFog: Leveraging Fog to Extend Cloud Gaming for Thin-Client MMOG with High Quality of Service,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 2, pp. 431–445, 2017, doi: 10.1109/TPDS.2016.2563428.
[36] S. Vlahovic, M. Suznjevic, and L. Skorin-Kapov, “The impact of network latency on gaming QoE for an FPS VR game,” in 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Jun. 2019, p. (pp.1-3). doi: 10.1109/QOMEX.2019.8743193.
[37] P. B. Beskow, K. H. Vik, P. Halvorsen, and C. Griwodz, “The partial migration of game state and dynamic server selection to reduce latency,” Multimed. Tools Appl., vol. 45, pp. 83–107, Oct. 2009, doi: 10.1007/s11042-009-0287-7.
[38] M. Claypool and D. Finkel, “The effects of latency on player performance in cloud-based games,” in 2014 13th Annual Workshop on Network and Systems Support for Games, 2014, p. (pp. 1-6). doi: 10.1109/NetGames.2014.7008964.
[39] S. Liu, M. Claypool, A. Kuwahara, J. Scovell, and J. Sherman, “The Effects of Network Latency on Competitive First-Person Shooter Game Players,” in 2021 13th International Conference on Quality of Multimedia Experience (QoMEX), 2021, pp. 151–156. doi: 10.1109/QoMEX51781.2021.9465419.
[40] Y. Liu, M. Huynh, A. Mangla, and D. Ghosal, “Performance analysis of adjustable discontinuous reception (DRX) mechanism in LTE network,” in 2014 23rd Wireless and Optical Communication Conference (WOCC), 2014, pp. 1–6. doi: 10.1109/WOCC.2014.6839948.
[41] J. R. Ky, B. Mathieu, A. Lahmadi, and R. Boutaba, “ML Models for Detecting QoE Degradation in Low-Latency Applications: A Cloud-Gaming Case Study,” IEEE Trans. Netw. Serv. Manag., vol. 20, no. 3, pp. 2295–2308, 2023, doi: 10.1109/TNSM.2023.3293806.
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Jul 31, 2024
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Adhistian, P., & Wibowo, P. (2024). QCI Optimization to Minimize Latency and Enhance User Experience. Jurnal Nasional Teknik Elektro, 13(2), 82–91. https://doi.org/10.25077/jnte.v13n2.1193.2024
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Telecommunication
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