Predição de Qualidade de Vídeo em Serviços de Streaming utilizando Redes 5G
Resumo
O surgimento das redes 5G provocou uma transformação significativa na sociedade contemporânea, evidenciando um notável crescimento, especialmente no setor de transmissão de vídeos em tempo real, mas também nos serviços de Streaming. Contudo, os usuários ainda sofrem com problemas de qualidade de vídeo inadequada, principalmente quando as características de transmissão das Redes 5G variam. Dentro deste contexto, este artigo apresenta um modelo de Inteligência Artificial (IA) para a predição da qualidade de vídeo entregue ao usuário final. O modelo proposto considera tanto cenários estáticos quanto cenários dinâmicos em dispositivos veiculares, reconhecendo a importância de compreender a entrega de conteúdo de acordo com a situação do usuário. A proposta deste trabalho utiliza dados de medições de Redes 5G, possibilitando a otimização da experiência do usuário em ambientes 5G dinâmicos. Os resultados mostram que a proposta é capaz de contribuir para o aprimoramento contínuo da entrega de qualidade de vídeo em redes móveis.Referências
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Portela, A. L. C., Ribeiro, S. E. S. B., Menezes, R. A., de Araujo, T., and Gomes, R. L. (2024). T-for: An adaptable forecasting model for throughput performance. IEEE Transactions on Network and Service Management, pages 1–1.
Raca, D., Leahy, D., Sreenan, C. J., and Quinlan, J. J. (2020). Beyond throughput, the next generation: a 5g dataset with channel and context metrics. In Proceedings of the 11th ACM multimedia systems conference, pages 303–308.
Riiser, H., Vigmostad, P., Griwodz, C., and Halvorsen, P. (2013). Commute path bandwidth traces from 3g networks: analysis and applications. In Proceedings of the 4th ACM Multimedia Systems Conference, pages 114–118.
Silveira, M. M., Portela, A. L., Menezes, R. A., Souza, M. S., Silva, D. S., Mesquita, M. C., and Gomes, R. L. (2023). Data protection based on searchable encryption and anonymization techniques. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–5.
Ukey, N., Yang, Z., Li, B., Zhang, G., Hu, Y., and Zhang, W. (2023). Survey on exact knn queries over high-dimensional data space. Sensors, 23(2):629.
Zhang, H., Dong, L., Gao, G., Hu, H., Wen, Y., and Guan, K. (2020). Deepqoe: A multimodal learning framework for video quality of experience (qoe) prediction. IEEE Transactions on Multimedia, 22(12):3210–3223.
Costa, V. G. and Pedreira, C. E. (2023). Recent advances in decision trees: An updated survey. Artificial Intelligence Review, 56(5):4765–4800.
Costa, W. L., Silveira, M. M., de Araujo, T., and Gomes, R. L. (2020). Improving ddos detection in iot networks through analysis of network traffic characteristics. In 2020 IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.
da Silva, G., Oliveira, D., Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. M. (2020). Reliable network slices based on elastic network resource demand. In NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, pages 1–9.
Duanmu, Z., Liu, W., Chen, D., Li, Z., Wang, Z., Wang, Y., and Gao, W. (2023). A bayesian quality-of-experience model for adaptive streaming videos. ACM Trans. Multimedia Comput. Commun. Appl., 18(3s).
Elsherbiny, H., Abbas, H. M., Abou-zeid, H., Hassanein, H. S., and Noureldin, A. (2020). 4g lte network throughput modelling and prediction. In GLOBECOM 2020-2020 IEEE Global Communications Conference, pages 1–6. IEEE.
Gomes, R., Bittencourt, L., Madeira, E., Cerqueira, E., and Gerla, M. (2017). Management of virtual network resources for multimedia applications. Multimedia Systems, 23(4):405–419.
Gomes, R., Junior, W., Cerqueira, E., and Abelem, A. (2010). A qoe fuzzy routing protocol for wireless mesh networks. In Zeadally, S., Cerqueira, E., Curado, M., and Leszczuk, M., editors, Future Multimedia Networking, pages 1–12, Berlin, Heidelberg. Springer Berlin Heidelberg.
Gomes, R. L., Bittencourt, L. F., Madeira, E. R., Cerqueira, E., and Gerla, M. (2016). A combined energy-bandwidth approach to allocate resilient virtual software defined networks. Journal of Network and Computer Applications, 69:98–106.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. M. (2020). Reliability-aware network slicing in elastic demand scenarios. IEEE Communications Magazine, 58(10):29–34.
Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media.
Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Automated machine learning: methods, systems, challenges. Springer Nature.
Irina, S., Irina, S., and Anastasiya, M. (2020). Forecasting 5g network multimedia traffic characteristics. In 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pages 982–987.
Jiang, M., Wang, J., Hu, L., and He, Z. (2023). Random forest clustering for discrete sequences. Pattern Recognition Letters, 174:145–151.
Kao, H.-W. and Wu, E. H.-K. (2023). Qoe sustainability on 5g and beyond 5g networks. IEEE Wireless Communications, 30(1):118–125.
Kousias, K., Rajiullah, M., Caso, G., Ali, U., Alay, O., Brunstrom, A., De Nardis, L., Neri, M., and Di Benedetto, M.-G. (2023). A large-scale dataset of 4g, nb-iot, and 5g non-standalone network measurements. IEEE Communications Magazine.
Moreira, D. A., Marques, H. P., Costa, W. L., Celestino, J., Gomes, R. L., and Nogueira, M. (2021). Anomaly detection in smart environments using ai over fog and cloud computing. In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), pages 1–2. IEEE.
Nightingale, J., Salva-Garcia, P., Calero, J. M. A., and Wang, Q. (2018). 5g-qoe: Qoe modelling for ultra-hd video streaming in 5g networks. IEEE Transactions on Broadcasting, 64(2):621–634.
Oliveira, D. H. L., Filho, F. M. V., de Araújo, T. P., Celestino, J., and Gomes, R. L. (2020). Adaptive model for network resources prediction in modern internet service providers. In 2020 IEEE Symposium on Computers and Communications (ISCC), pages 1–6.
Portela, A. L., Menezes, R. A., Costa, W. L., Silveira, M. M., Bittecnourt, L. F., and Gomes, R. L. (2023). Detection of iot devices and network anomalies based on anonymized network traffic. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–6.
Portela, A. L. C., Ribeiro, S. E. S. B., Menezes, R. A., de Araujo, T., and Gomes, R. L. (2024). T-for: An adaptable forecasting model for throughput performance. IEEE Transactions on Network and Service Management, pages 1–1.
Raca, D., Leahy, D., Sreenan, C. J., and Quinlan, J. J. (2020). Beyond throughput, the next generation: a 5g dataset with channel and context metrics. In Proceedings of the 11th ACM multimedia systems conference, pages 303–308.
Riiser, H., Vigmostad, P., Griwodz, C., and Halvorsen, P. (2013). Commute path bandwidth traces from 3g networks: analysis and applications. In Proceedings of the 4th ACM Multimedia Systems Conference, pages 114–118.
Silveira, M. M., Portela, A. L., Menezes, R. A., Souza, M. S., Silva, D. S., Mesquita, M. C., and Gomes, R. L. (2023). Data protection based on searchable encryption and anonymization techniques. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–5.
Ukey, N., Yang, Z., Li, B., Zhang, G., Hu, Y., and Zhang, W. (2023). Survey on exact knn queries over high-dimensional data space. Sensors, 23(2):629.
Zhang, H., Dong, L., Gao, G., Hu, H., Wen, Y., and Guan, K. (2020). Deepqoe: A multimodal learning framework for video quality of experience (qoe) prediction. IEEE Transactions on Multimedia, 22(12):3210–3223.
Publicado
20/05/2024
Como Citar
PIMENTA, Ivo A.; SOUZA, Michael S.; AQUINO, Carlos A.; PORTELA, Ariel L.; GOMES, Rafael L..
Predição de Qualidade de Vídeo em Serviços de Streaming utilizando Redes 5G. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 8. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 43-56.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2024.2882.
