University of Illinois at Urbana-Champaign
Saliency-aware viewport-guidance-enabled 360-video streaming system
Abstract
dc:descriptionThe emergence of 360-video streaming systems has brought about new possibilities for immersive video experiences while requiring significantly higher bandwidth than traditional 2D video streaming. Existing viewport prediction methods, while attempting to tackle this challenge, often overlook compelling storylines outside the immediate viewport. To address this limitation, we introduce SAVG360, an innovative viewport guidance system leveraging global content information on the server side to enrich streaming with the most saliently captured storyline of 360-videos. The saliency analysis is conducted offline on a powerful GPU-equipped media server, and the resulting saliency-aware guidance information is encoded and shared with clients via the Saliency-aware Guidance Descriptor. This proactive guidance system empowers users to seamlessly switch between video storylines, providing the flexibility to either adhere to or deviate from guided narratives through a novel user interface. Additionally, our contribution encompasses a Viewing Mode Prediction algorithm and a Least Saliency (LS) caching policy, both designed to enhance video delivery within the SAVG360 framework. Evaluation based on user viewport traces in 360-videos reveals that SAVG360 outperforms existing tiled streaming solutions, demonstrating superior overall viewport prediction accuracy and the ability to deliver high-quality 360 videos within bandwidth constraints. Moreover, SAVG360 enhances the hit rate of requested tiles through the utilization of the LS caching policy when compared to traditional cache replacement policies. Furthermore, a user study highlights the advantages of our proactive guidance approach over conventional methods that predict and stream content based on users' gaze direction.
Degree
thesis:*- Name thesis:degree_name
- M.S.
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2023
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Zhang, Yinjie
- Contributors dc:contributor
-
- Nahrstedt, Klara
Subjects
dc:subject × 4Rights
dc:rights- Statement dc:rights
-
- Copyright 2023 Yinjie Zhang
- Language dc:language
- en, eng
Identifiers
dc:identifier.*- Handle dc:identifier
- https://hdl.handle.net/2142/122178