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Quality of Experience Oriented Cross-layer Optimization for Real-time XR Video Transmission (2404.09905v1)

Published 15 Apr 2024 in cs.NI, cs.MM, cs.SY, eess.IV, and eess.SY

Abstract: Extended reality (XR) is one of the most important applications of beyond 5G and 6G networks. Real-time XR video transmission presents challenges in terms of data rate and delay. In particular, the frame-by-frame transmission mode of XR video makes real-time XR video very sensitive to dynamic network environments. To improve the users' quality of experience (QoE), we design a cross-layer transmission framework for real-time XR video. The proposed framework allows the simple information exchange between the base station (BS) and the XR server, which assists in adaptive bitrate and wireless resource scheduling. We utilize the cross-layer information to formulate the problem of maximizing user QoE by finding the optimal scheduling and bitrate adjustment strategies. To address the issue of mismatched time scales between two strategies, we decouple the original problem and solve them individually using a multi-agent-based approach. Specifically, we propose the multi-step Deep Q-network (MS-DQN) algorithm to obtain a frame-priority-based wireless resource scheduling strategy and then propose the Transformer-based Proximal Policy Optimization (TPPO) algorithm for video bitrate adaptation. The experimental results show that the TPPO+MS-DQN algorithm proposed in this study can improve the QoE by 3.6% to 37.8%. More specifically, the proposed MS-DQN algorithm enhances the transmission quality by 49.9%-80.2%.

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References (37)
  1. H. Ning, H. Wang, Y. Lin, W. Wang, S. Dhelim, F. Farha, J. Ding, and M. Daneshmand, “A survey on metaverse: the state-of-the-art, technologies, applications, and challenges,” arXiv preprint arXiv:2111.09673, 2021.
  2. 3GPP TR 26.928, “Technical Specification Group Services and System Aspects; Extended Reality (XR) in 5G (Release 18),” V18.0.0, Mar. 2023.
  3. W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Netw., vol. 34, no. 3, pp. 134–142, 2020.
  4. H. Mao, R. Netravali, and M. Alizadeh, “Neural adaptive video streaming with pensieve,” in Proc. SIGCOMM’17, 2017, p. 197–210.
  5. G. Carlucci, L. De Cicco, S. Holmer, and S. Mascolo, “Congestion control for web real-time communication,” IEEE/ACM Trans. Netw., vol. 25, no. 5, pp. 2629–2642, 2017.
  6. 3GPP TR 38.838, “Technical Specification Group Radio Access Network; Study on XR (Extended Reality) Evaluations for NR (Release 17),” V17.0.0, Dec. 2021.
  7. B. Liu, H. Tian, and L. Xu, “An efficient downlink packet scheduling algorithm for real time traffics in LTE systems,” in Proc. CCNC’2013, 2013, pp. 364–369.
  8. N. Sharma, S. Zhang, S. R. S. Venkata, F. Malandra, N. Mastronarde, and J. Chakareski, “Deep reinforcement learning for delay-sensitive LTE downlink scheduling,” in Proc. PIMRC’2020, 2020, pp. 1–6.
  9. J.-G. Choi and S. Bahk, “Cell-throughput analysis of the proportional fair scheduler in the single-cell environment,” IEEE Trans. Veh. Technol., vol. 56, no. 2, pp. 766–778, 2007.
  10. E. Chen, S. Dou, S. Wang, Y. Cao, and S. Liao, “Frame-level integrated transmission for extended reality over 5G and beyond,” in Proc. GLOBECOM’2021, 2021, pp. 1–6.
  11. W. Liu, H. Zhang, H. Ding, Z. Yu, and D. Yuan, “Qoe-aware collaborative edge caching and computing for adaptive video streaming,” IEEE Trans. Wireless Commun., pp. 1–1, 2023.
  12. M. Naresh, P. Saxena, and M. Gupta, “PPO-ABR: Proximal policy optimization based deep reinforcement learning for adaptive bitrate streaming,” in Proc. IWCMC’2023, 2023, pp. 199–204.
  13. S. Shakkottai and A. L. Stolyar, “Scheduling for multiple flows sharing a time-varying channel: The exponential rule,” Translations Amer. Math. Soc., vol. 207, pp. 185–202, 2002.
  14. Y. Hao, F. Li, C. Zhao, and S. Yang, “Delay-oriented scheduling in 5G downlink wireless networks based on reinforcement learning with partial observations,” IEEE/ACM Trans. Netw., pp. 1–15, 2022.
  15. X. Yin, A. Jindal, V. Sekar, and B. Sinopoli, “A control-theoretic approach for dynamic adaptive video streaming over HTTP,” in Proc. SIGCOMM’15, 2015, p. 325–338.
  16. K. Spiteri, R. Urgaonkar, and R. K. Sitaraman, “BOLA: Near-optimal bitrate adaptation for online videos,” IEEE/ACM Trans. Netw., vol. 28, no. 4, pp. 1698–1711, 2020.
  17. Y. Li, X. Zhang, C. Cui, S. Wang, and S. Ma, “Fleet: Improving quality of experience for low-latency live video streaming,” IEEE Trans. Circuits Syst. Video Technol., pp. 1–1, 2023.
  18. Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran, “Probe and adapt: Rate adaptation for HTTP video streaming at scale,” IEEE J. Sel. Areas Commun., vol. 32, no. 4, pp. 719–733, 2014.
  19. H. Yuan, H. Lu, L. Meng, and M. Liu, “Muabr: Multi-user adaptive bitrate algorithm based multi-agent deep reinforcement learning,” in Proc. ICC’2022, 2022, pp. 751–756.
  20. N. Li, Y. Hu, Y. Chen, and B. Zeng, “Lyapunov optimized resource management for multiuser mobile video streaming,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 6, pp. 1795–1805, 2019.
  21. K. Tang, N. Kan, J. Zou, C. Li, X. Fu, M. Hong, and H. Xiong, “Multi-user adaptive video delivery over wireless networks: A physical layer resource-aware deep reinforcement learning approach,” IEEE Trans. Circuits Syst. Video Technol., vol. 31, no. 2, pp. 798–815, 2021.
  22. J. Yu, H. Wen, G. Pan, S. Zhang, X. Chen, and S. Xu, “Quality of experience oriented adaptive video streaming for edge assisted cellular networks,” IEEE Wireless Commun. Lett., vol. 11, no. 11, pp. 2305–2309, 2022.
  23. K. Xiao, S. Mao, and J. K. Tugnait, “Robust QoE-driven DASH over OFDMA networks,” IEEE Trans. Multimedia, vol. 22, no. 2, pp. 474–486, 2020.
  24. C. He, S. Zhu, and B. Zeng, “NOMA-based uncoded video transmission with optimization of joint resource allocation,” IEEE Trans. Circuits Syst. Video Technol., vol. 33, no. 5, pp. 2439–2450, 2023.
  25. S. Wang, S. Bi, and Y.-J. A. Zhang, “Adaptive wireless video streaming: Joint transcoding and transmission resource allocation,” IEEE Trans. Wireless Commun., vol. 21, no. 5, pp. 3208–3221, 2022.
  26. D. Wang, L. Toni, P. C. Cosman, and L. B. Milstein, “Uplink resource management for multiuser ofdm video transmission systems: Analysis and algorithm design,” IEEE Trans. Commun., vol. 61, no. 5, pp. 2060–2073, 2013.
  27. S.-M. Tseng, G.-Y. Chen, and H.-C. Chan, “Cross-layer resource management for downlink bf-noma-ofdma video transmission systems and supervised/unsupervised learning based approach,” IEEE Trans. Veh. Technol., vol. 71, no. 10, pp. 10 744–10 753, 2022.
  28. F. Li, T. Wang, and P. C. Cosman, “Joint rate adaptation and resource allocation for real-time h. 265/hevc video transmission over uplink ofdma systems,” Multimedia Tools and Applications, vol. 78, pp. 26 807–26 831, 2019.
  29. H. Zhang, A. Zhou, and H. Ma, “Improving mobile interactive video QoE via two-level online cooperative learning,” IEEE Trans. Mobile Comput., pp. 1–1, 2022.
  30. Y.-H. Jung, Q. Song, K.-H. Kim, P. Cosman, and L. B. Milstein, “Cross-layer resource allocation using video slice header information for wireless transmission over LTE,” IEEE Trans. Circuits Syst. Video Technol., vol. 28, no. 8, pp. 2024–2037, 2018.
  31. W. Yin, L. Xu, W. Liu, Z. Cai, Y. Yang, and P. Wang, “Joint video packet assignment, power control and user scheduling over cognitive multi-homing heterogeneous noma networks,” IEEE Trans. Circuits Syst. Video Technol., vol. 32, no. 7, pp. 4724–4735, 2022.
  32. H. Schulzrinne, S. L. Casner, R. Frederick, and V. Jacobson, “RTP: A transport protocol for real-time applications,” IETF, July 2003.
  33. Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas, “Dueling network architectures for deep reinforcement learning,” in Proc. ICML’2016, 2016, pp. 1995–2003.
  34. Y. Chen and H. Zhang, “Power allocation based on deep reinforcement learning in hetnets with varying user activity,” in Proc. GLOBECOM’2020, 2020, pp. 1–6.
  35. J. Song, Q. Song, Y. Kang, L. Guo, and A. Jamalipour, “QoE-driven distributed resource optimization for mixed reality in dynamic TDD systems,” IEEE Trans. Commun., pp. 1–1, 2022.
  36. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017.
  37. 3GPP TR 38.901, “Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz (Release 17),” V17.0.0, Mar. 2022.
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