International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Dynamic Path Selection Based Video Transmission in User Preference Assisted Adaptive Rate Control in 5G Multi-RAT Network


Affiliations
1 Department of Computer Science and Engineering, JNT University, Anantapuram, Andhra Pradesh, India
2 Department of Computer Science and Engineering, G. Pulla Reddy Engineering College Kurnool, Andhra Pradesh, India
 

5G mobile users consume a large amount of video content. Providing high-quality video to mobile users via a single path is a challenging task. It takes much time to transmit the video. In this paper, we proposed dynamic path selection-based video transmission is proposed for a 5G multi-RAT network. In our proposed system has four consecutive processes, which are discussed as follows: (1) Optimal access point selection which is done by Red Deer Algorithm (RDA) that selects optimal access point by considering data rate, Signal to Inference Noise Ratio (SINR) and Received Signal Strength Indicator(RSSI) to achieve better Quality of Experience (QoE). (2) Adaptive Video Encoding, for this purpose, we useH.265 encoding algorithm, which encodes the video packets in order to reduce transmission time and bandwidth consumption, here bit rate is adaptively controlled using the SARSA reinforcement algorithm, by considering the network environment factors ( bit error rate, attenuation, bandwidth, throughput, and SNR) and user preference factors (high/low quality and processing speed). In this stage, the SWARA decision-making algorithm is used to select optimal QP parameters for each video packet, which considers three parameters: distortion, previous QP value, and CSI, which improves the quality of the video. (3). Dynamic path selection is made using the Deng-based Type 2 Fuzzy algorithm (Deng-T2F), which selects the optimal path between source and destination based on the following parameters: the number of hops, link stability, and buffer size increases high throughput and reduce transmission delay. (4). Adaptive Buffer Management is proposed for reducing latency during video transmission. The Adaptive Pre-order Deficit Round Robin (ADPDRR) algorithm is used to evaluate the parameters of layer information, deadline, packet size, and arrival time to reduce packet loss and packet waiting time during video transmission. The proposed APDRR algorithm maintains three queues based on the packet priority, and then the prioritized packets are transmitted adaptively to reduce the packet waiting time. Finally, simulation is conducted using an NS-3.26 network simulator that evaluates the performance based on the following metrics: PSNR, MoS, bandwidth utilization, jitter, Throughput, Delay, Packet drop rate, and Goodput.

Keywords

Dynamic Path Selection, 5G-Multi-RAT, Red Deer Algorithm, SARSA, SWARA Decision Making, Adaptive Buffer Management.
User
Notifications
Font Size

  • Yang, Q., & Yang, J. (2020). HD video transmission of multi-rotor Unmanned Aerial Vehicle based on 5G cellular communication network. Comput. Commun., 160, 688-696.

  • Salva-Garcia, P., Alcaraz-Calero, J.M., Wang, Q., Arevalillo-Herráez, M., & Bernabe, J.B. (2020). Scalable Virtual Network Video-Optimizer for Adaptive Real-Time Video Transmission in 5G Networks. IEEE Transactions on Network and Service Management, 17, 1068-1081

  • Maung, Htoo & Aramvith, Supavadee & Miyanaga, Yoshikazu. (2019). Hierarchical-P Reference Picture Selection Based Error Resilient Video Coding Framework for High-Efficiency Video Coding Transmission Applications. Electronics. 8. 310. 10.3390/electronics8030310.

  • Galiano, D.R., Barrio, A., Juan, G., & Cuesta, D. (2020). Securing high-resolution train videos encoded with HEVC and inter prediction mode. Comput. Ind., 121, 103258.

  • Deng, K., Yuan, L., Wan, Y., & Pan, J. (2018). Optimized cross-layer transmission for scalable video over DVB-H networks. Signal Process. Image Commun., 63, 81-91.

  • Li, H., Lei, W., Zhang, W., & Guan, Y. (2019). A joint optimization method of coding and transmission for conversational HD video service. Comput. Commun., 145, 243-262.

  • Poularakis, K., Iosifidis, G., Argyriou, A., Koutsopoulos, I., & Tassiulas, L. (2019). Distributed Caching Algorithms in the Realm of Layered Video Streaming. IEEE Transactions on Mobile Computing, 18, 757-770..

  • Tian, D., Zhang, C., Duan, X., Wang, Y., Zhou, J., & Sheng, Z. (2019). A multi-hop routing protocol for video transmission in IoVs based on cellular attractor selection. Future Gener. Comput. Syst., 95, 713-726.

  • More, S., Naik, U., Malemath, V.S., & Kumar, P. (2020). Efficient multi-path driven lossless video transmission for VANETS. Materials Today: Proceedings.

  • Zhang, B., Cosman, P., & Milstein, L. (2019). Energy Optimization For Wireless Video Transmission Employing Hybrid ARQ. IEEE Transactions on Vehicular Technology, 68, 5606-5617.

  • Chen, F., Zhang, J., Chen, Z., Wu, J., & Ling, N. (2019). Buffer-Driven Rate Control and Packet Distribution for Real-Time Videos in Heterogeneous Wireless Networks. IEEE Access, 7, 27401-27415.

  • Li, P., Yang, F., Zhang, J., Guan, Y., Wang, A., & Liang, J. (2020). Synthesis-Distortion-Aware Hybrid Digital Analog Transmission for 3D Videos. IEEE Access, 8, 85128-85139.

  • Wang, Z., Hou, Z., Hu, R., & Xiao, J. (2019). A Lossless Recompression Approach for Video Streaming Transmission. IEEE Access, 7, 35162-35172.

  • Guo, J., Gong, X., Liang, J., Wang, W., & Que, X. (2020). EAAT: Environment-Aware Adaptive Transmission for Split-Screen Video Streaming. IEEE Transactions on Circuits and Systems for Video Technology, 30, 4355-4367.

  • Maimour, M. (2018). SenseVid : A traffic trace based tool for QoE Video transmission assessment dedicated to Wireless Video Sensor Networks. Simul. Model. Pract. Theory, 87, 120-137.

  • Nightingale, J., Salva-Garcia, P., Calero, J. M. A., & Wang, Q. (2018). 5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks. IEEE Transactions on Broadcasting, 64(2), 621–634.

  • Amine, M., Kobbane, A., Ben-Othman, J., &Walid, A. (2020). Two-sided matching framework for optimal user association in 5G multi-RAT UDNs. Int. J. Commun. Syst., 33.

  • Habbal, A., Goudar, S.I., & Hassan, S. (2017). Context-Aware Radio Access Technology Selection in 5G Ultra-Dense Networks. IEEE Access, 5, 6636-6648.

  • Yu, Y.-J., Pang, A.-C., & Yeh, M.-Y. (2018). Video encoding adaptation for QoE maximization over 5G cellular networks. Journal of Network and Computer Applications, 114, 98–107.

  • Yan, T., Ra, I.-H., Zhang, Q., Xu, H., & Huang, L. (2020). A Novel Rate Control Algorithm Based on ρ Model for Multiview High Efficiency Video Coding. Electronics, 9(1), 166.

  • Lu, W., Yu, D., Huang, M., & Guo, B. (2018). PO-MPTCP: Priorities-Oriented Data Scheduler for Multimedia Multipathing Services. Int. J. Digit. Multim. Broadcast., 2018, 1413026:1-1413026:9.

  • Lu, Z., & Veciana, G.D. (2019). Optimizing Stored Video Delivery for Wireless Networks: The Value of Knowing the Future. IEEE Transactions on Multimedia, 21, 197-210.

  • Rodrigues, F., Sousa, I., Queluz, M. P., & Rodrigues, A. (2018). QoE-Aware Scheduling Algorithm for Adaptive HTTP Video Delivery in Wireless Networks. Wireless Communications and Mobile Computing, 2018, 1–16.

  • Ali, A., Tariq, S., Iqbal, M.K., Feng, L., Raza, I., Siddiqi, M., & Bashir, A. (2020). Adaptive Bitrate Video Transmission Over Cognitive Radio Networks Using Cross Layer Routing Approach. IEEE Transactions on Cognitive Communications and Networking, 6, 935-945.

  • Ma, J., Liu, L., Song, H., Shafin, R., Shang, B., & Fan, P. (2020). Scalable Video Transmission in Cache-Aided Device-to-Device Networks. IEEE Transactions on Wireless Communications, 19, 4247-4261.

  • Wen, T., Cai, W., Wang, A., Liang, J., & Zhao, L. (2020). HDA Video Transmission Scheme for DASH. IEEE Access, 8, 58345-58356.

  • D.G., N., Naravani, M., & Shinde, S. (2020). Cross-layer Optimization for Video Transmission using MDC in Wireless Mesh Networks. Procedia Computer Science, 171, 282–291. doi:10.1016/j.procs.2020.04.030

  • Yuan, J. (2019). Video data wireless transmission method based on cross-layer bitrate adaptation and error control. Multimedia Tools and Applications, 1-12.

  • Wu, J., Tan, R., & Wang, M. (2019). Streaming High-Definition Real-Time Video to Mobile Devices with Partially Reliable Transfer. IEEE Transactions on Mobile Computing, 18, 458-472.

  • Song, S., Jung, J., Choi, M., Lee, C., Sun, J., & Chung, J.-M. (2019). Multipath Based Adaptive Concurrent Transfer for Real-Time Video Streaming Over 5G Multi-RAT Systems. IEEE Access, 7, 146470–146479.

  • Elgabli, A., & Aggarwal, V. (2019). SmartStreamer: Preference-Aware Multipath Video Streaming Over MPTCP. IEEE transactions on vehicular technology, vol. 68, no. 7, july 2019.


Abstract Views: 231

PDF Views: 1




  • Dynamic Path Selection Based Video Transmission in User Preference Assisted Adaptive Rate Control in 5G Multi-RAT Network

Abstract Views: 231  |  PDF Views: 1

Authors

M. Muni Babu
Department of Computer Science and Engineering, JNT University, Anantapuram, Andhra Pradesh, India
R. Praveen Sam
Department of Computer Science and Engineering, G. Pulla Reddy Engineering College Kurnool, Andhra Pradesh, India
P. Chenna Reddy
Department of Computer Science and Engineering, JNT University, Anantapuram, Andhra Pradesh, India

Abstract


5G mobile users consume a large amount of video content. Providing high-quality video to mobile users via a single path is a challenging task. It takes much time to transmit the video. In this paper, we proposed dynamic path selection-based video transmission is proposed for a 5G multi-RAT network. In our proposed system has four consecutive processes, which are discussed as follows: (1) Optimal access point selection which is done by Red Deer Algorithm (RDA) that selects optimal access point by considering data rate, Signal to Inference Noise Ratio (SINR) and Received Signal Strength Indicator(RSSI) to achieve better Quality of Experience (QoE). (2) Adaptive Video Encoding, for this purpose, we useH.265 encoding algorithm, which encodes the video packets in order to reduce transmission time and bandwidth consumption, here bit rate is adaptively controlled using the SARSA reinforcement algorithm, by considering the network environment factors ( bit error rate, attenuation, bandwidth, throughput, and SNR) and user preference factors (high/low quality and processing speed). In this stage, the SWARA decision-making algorithm is used to select optimal QP parameters for each video packet, which considers three parameters: distortion, previous QP value, and CSI, which improves the quality of the video. (3). Dynamic path selection is made using the Deng-based Type 2 Fuzzy algorithm (Deng-T2F), which selects the optimal path between source and destination based on the following parameters: the number of hops, link stability, and buffer size increases high throughput and reduce transmission delay. (4). Adaptive Buffer Management is proposed for reducing latency during video transmission. The Adaptive Pre-order Deficit Round Robin (ADPDRR) algorithm is used to evaluate the parameters of layer information, deadline, packet size, and arrival time to reduce packet loss and packet waiting time during video transmission. The proposed APDRR algorithm maintains three queues based on the packet priority, and then the prioritized packets are transmitted adaptively to reduce the packet waiting time. Finally, simulation is conducted using an NS-3.26 network simulator that evaluates the performance based on the following metrics: PSNR, MoS, bandwidth utilization, jitter, Throughput, Delay, Packet drop rate, and Goodput.

Keywords


Dynamic Path Selection, 5G-Multi-RAT, Red Deer Algorithm, SARSA, SWARA Decision Making, Adaptive Buffer Management.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F209984