Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video
The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm....
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| Vydáno v: | Computers, materials & continua Ročník 71; číslo 2; s. 2617 - 2631 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Henderson
Tech Science Press
2022
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| ISSN: | 1546-2226, 1546-2218, 1546-2226 |
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| Abstract | The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm. However, the users only focus on the part of 360-degree videos, known as a viewport. Despite the immense hype, 360-degree videos convey a loathsome side effect about viewport prediction, making viewers feel uncomfortable because user viewport needs to be pre-fetched in advance. Ideally, we can minimize the bandwidth consumption if we know what the user motion in advance. Looking into the problem definition, we propose an Encoder-Decoder based Long-Short Term Memory (LSTM) model to more accurately capture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct input to predict the future user movement. Then, this prediction model is combined with a rate adaptation approach that assigns the bitrates to various tiles for 360-degree video frames under a given network capacity. Hence, our proposed work aims to facilitate improved system performance when QoE parameters are jointly optimized. Some experiments were carried out and compared with existing work to prove the performance of the proposed model. Last but not least, the experiments implementation of our proposed work provides high user's QoE than its competitors. |
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| AbstractList | The development of multimedia content has resulted in a massive increase in network traffic for video streaming. It demands such types of solutions that can be addressed to obtain the user's Quality-of-Experience (QoE). 360-degree videos have already taken up the user's behavior by storm. However, the users only focus on the part of 360-degree videos, known as a viewport. Despite the immense hype, 360-degree videos convey a loathsome side effect about viewport prediction, making viewers feel uncomfortable because user viewport needs to be pre-fetched in advance. Ideally, we can minimize the bandwidth consumption if we know what the user motion in advance. Looking into the problem definition, we propose an Encoder-Decoder based Long-Short Term Memory (LSTM) model to more accurately capture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct input to predict the future user movement. Then, this prediction model is combined with a rate adaptation approach that assigns the bitrates to various tiles for 360-degree video frames under a given network capacity. Hence, our proposed work aims to facilitate improved system performance when QoE parameters are jointly optimized. Some experiments were carried out and compared with existing work to prove the performance of the proposed model. Last but not least, the experiments implementation of our proposed work provides high user's QoE than its competitors. |
| Author | Aleem Jamali, Abdul Rizwan Anjum, Muhammad Rafiq, Ammar Afridi, Sharjeel Ali Arain, Zulfiqar Usman Younus, Muhammad Shafi, Rabia |
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| Cites_doi | 10.1145/3362101 10.3390/sym12091491 10.1109/ACCESS.2020.3046693 10.1109/TPAMI.2018.2858783 10.1109/TNSM.2021.3053183 |
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| DOI | 10.32604/cmc.2022.022236 |
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| References | Nguyen (ref11) 2018 Kan (ref16) 2021; 31 Park (ref12) 2021; 18 Xie (ref27) 2017 Goodfellow (ref24) 2016; 1 ref1 Shafi (ref30) 2020 Jiang (ref5) 2018 Jiang (ref23) 2017 Qian (ref26) 2018 Jun (ref18) 2019 Tang (ref4) 2020 He (ref2) 2018 Wu (ref20) 2016 Younus (ref14) 2020; 9 Lo (ref21) 2017 Qian (ref28) 2015 Kan (ref19) 2019 Park (ref25) 2021 Hooft (ref3) 2019; 15 Xu (ref13) 2018; 41 Xie (ref6) 2017 Younus (ref15) 2021; 10 Bao (ref22) 2016 Nasrabadi (ref7) 2017 Younus (ref17) 2020 Zhang (ref8) 2019 Shafi (ref29) 2020; 12 Chopra (ref9) 2021 Aladagli (ref10) 2017 |
| References_xml | – start-page: 1 year: 2020 ident: ref4 article-title: A viewport prediction framework for panoramic videos – ident: ref1 – start-page: 393 year: 2018 ident: ref5 article-title: Plato: Learning-based adaptive streaming of 360-degree videos – start-page: 1252 year: 2019 ident: ref8 article-title: DRL360: 360-degree video streaming with deep reinforcement learning – volume: 15 start-page: 1 year: 2019 ident: ref3 article-title: Tile-based adaptive streaming for virtual reality video publication-title: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) doi: 10.1145/3362101 – start-page: 211 year: 2017 ident: ref21 article-title: 360 video viewing dataset in head-mounted virtual reality – volume: 10 start-page: 14546 year: 2021 ident: ref15 article-title: Improving the software defined wireless sensor networks routing performance using reinforcement learning publication-title: IEEE Internet of Things Journal – start-page: 1 year: 2017 ident: ref10 article-title: Predicting head trajectories in 360 virtual reality videos – start-page: 1839 year: 2021 ident: ref25 article-title: Adaptive streaming of 360-degree videos with reinforcement learning – volume: 31 start-page: 3631 year: 2021 ident: ref16 article-title: RAPT360: Reinforcement learning-based rate adaptation for 360-degree video streaming with adaptive prediction and tiling publication-title: IEEE Transactions on Circuits and Systems for Video Technology – volume: 12 start-page: 1 year: 2020 ident: ref29 article-title: 360-degree video streaming: A survey of the state of the art publication-title: Symmetry doi: 10.3390/sym12091491 – start-page: 1868 year: 2020 ident: ref30 article-title: MTC360: A multi-tiles configuration for viewport-dependent 360-gegree video streaming – volume: 9 start-page: 259 year: 2020 ident: ref14 article-title: Optimizing the lifetime of software defined wireless sensor network via reinforcement learning publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3046693 – start-page: 290 year: 2019 ident: ref18 article-title: 360SRL: A sequential reinforcement learning approach for ABR tile-based 360 video streaming – start-page: 315 year: 2017 ident: ref6 article-title: 360probdash: Improving lSTM of 360 video streaming using tile-based http adaptive streaming – volume: 41 start-page: 2693 year: 2018 ident: ref13 article-title: Predicting head movement in panoramic video: A deep reinforcement learning approach publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2018.2858783 – volume: 1 year: 2016 ident: ref24 publication-title: Deep Learning – start-page: 1 year: 2016 ident: ref20 article-title: Video saliency prediction with optimized optical flow and gravity center bias – start-page: 1161 year: 2016 ident: ref22 article-title: Shooting a moving target: Motion-prediction-based transmission for 360-degree videos – start-page: 315 year: 2017 ident: ref27 article-title: 360 probdash: Improving QoE of 360 video streaming using tile-based http adaptive streaming – start-page: 1689 year: 2017 ident: ref7 article-title: Adaptive 360-degree video streaming using scalable video coding – volume: 18 start-page: 1000 year: 2021 ident: ref12 article-title: Mosaic: Advancing user quality of experience in 360-degree video streaming with machine learning publication-title: IEEE Transactions on Network and Service Management doi: 10.1109/TNSM.2021.3053183 – start-page: 2379 year: 2021 ident: ref9 article-title: PARIMA: Viewport adaptive 360-degree video streaming – start-page: 1190 year: 2018 ident: ref11 article-title: Your attention is unique: Detecting 360-degree video saliency in head-mounted display for head movement prediction – start-page: 4030 year: 2019 ident: ref19 article-title: Deep reinforcement learning-based rate adaptation for adaptive 360-degree video streaming – start-page: 482 year: 2018 ident: ref2 article-title: Rubiks: Practical 360-degree streaming for smartphones – start-page: 1 year: 2015 ident: ref28 article-title: Optimizing 360 video delivery over cellular networks – year: 2020 ident: ref17 publication-title: Contribution to energy optimization in WSN: Routing based on RL and SDN oriented routing – start-page: 658 year: 2017 ident: ref23 article-title: A hybrid algorithm of adaptive particle swarm optimization based on adaptive moment estimation method – start-page: 99 year: 2018 ident: ref26 article-title: Flare: Practical viewport-adaptive 360-degree video streaming for mobile devices |
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| Title | Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video |
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