A Comparative Analysis of Viewing Prediction Techniques for 360° Video Streaming Applications

  • Moatasim Mahmoud (Speaker)
  • Stamatia Rizou (Contributor to Paper or Presentation)
  • Andreas S. Panayides (Contributor to Paper or Presentation)
  • Lazaridis, P. (Contributor to Paper or Presentation)
  • Nikolaos V. Kantartzis (Contributor to Paper or Presentation)
  • George K. Karagiannidis (Contributor to Paper or Presentation)
  • Zaharias D. Zaharis (Contributor to Paper or Presentation)

Activity: Talk or presentation typesOral presentation


In this work, we implement multiple techniques for predicting users viewing directions while watching 360° videos. We utilize historical viewing traces to forecast future directions based on a real-life head tracking dataset. We compare the performance of linear regression (LR), artificial neural networks (ANN), long short-term memory (LSTM), and convolutional neural networks (CNN). We assess their efficiency in terms of viewing angles prediction errors. We also investigate tile viewing prediction in tile-based 360° video transmission scenarios. We built two classifiers based on ANN and LSTM to predict watched tiles and provide an evaluation of their performance in this article.
Period28 Mar 2024
Event title2024 Panhellenic Conference on Electronics and Telecommunications
Event typeConference
LocationThessaloniki, GreeceShow on map
Degree of RecognitionInternational