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

作　　者：Muhammad Usman Younus Rabia Shafi Ammar Rafiq Muhammad Rizwan Anjum Sharjeel Afridi Abdul Aleem Jamali Zulfiqar Ali Arain 

机构地区：[1]Ecole Doctorale Mathematiques,Informatique,Telecommunication,de Toulouse,University Paul Sabatier,Toulouse,31330,France [2]School of Electronics and Information,Northwestern Polytechnical University,Xi’an,710129,China [3]Department of Computer Science,NFC Institute of Engineering and Fertilizer Research,Faisalabad,38000,Pakistan [4]Department of Electronic Engineering,The Islamia University of Bahawalpur,Bahawalpur,63100,Pakistan [5]Department of Electrical Engineering,Sukkur IBA University,Sukkur,65200,Pakistan [6]Department of Electronic Engineering,Quaid-e-Awam University of Engineering,Science and Technology(QUEST),Nawabshah,67450,Pakistan [7]Department of Telecommunication Engineering,MUET,Jamshoro,76060,Pakistan

出　　处：《Computers, Materials & Continua》2022年第5期2617-2631,共15页计算机、材料和连续体（英文）

摘　　要：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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]