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

Computers, Materials & Continua 2022年第5期71卷 2617-2631页

作者： Muhammad Usman Younus rabia shafi Ammar Rafiq Muhammad Rizwan Anjum Sharjeel Afridi Abdul Aleem Jamali Zulfiqar Ali Arain Ecole Doctorale Mathematiques InformatiqueTelecommunicationde ToulouseUniversity Paul SabatierToulouse31330France School of Electronics and Information Northwestern Polytechnical UniversityXi’an710129China Department of Computer Science NFC Institute of Engineering and Fertilizer ResearchFaisalabad38000Pakistan Department of Electronic Engineering The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Electrical Engineering Sukkur IBA UniversitySukkur65200Pakistan Department of Electronic Engineering Quaid-e-Awam University of EngineeringScience and Technology(QUEST)Nawabshah67450Pakistan Department of Telecommunication Engineering MUETJamshoro76060Pakistan

The development of multimedia content has resulted in a massiveincrease in network traffic for video streaming. It demands such types ofsolutions 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 aviewport. Despite the immense hype, 360-degree videos convey a loathsomeside effect about viewport prediction, making viewers feel uncomfortablebecause user viewport needs to be pre-fetched in advance. Ideally, we canminimize the bandwidth consumption if we know what the user motionin advance. Looking into the problem definition, we propose an EncoderDecoder based Long-Short Term Memory (LSTM) model to more accuratelycapture the non-linear relationship between past and future viewport positions. This model takes the transforming data instead of taking the direct inputto predict the future user movement. Then, this prediction model is combinedwith a rate adaptation approach that assigns the bitrates to various tiles for360-degree video frames under a given network capacity. Hence, our proposedwork aims to facilitate improved system performance when QoE parametersare jointly optimized. Some experiments were carried out and compared withexisting work to prove the performance of the proposed model. Last but notleast, the experiments implementation of our proposed work provides highuser’s QoE than its competitors.

关键词： Encoder-decoder based lSTM 360-degree video streaming LSTM QoE viewport prediction

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