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 |
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| Title | Encoder-Decoder Based LSTM Model to Advance User QoE in 360-Degree Video |
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