English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose a robust scheme for streaming 360-degree immersive videos tomaximize the quality of experience (QoE). Our streaming approach introduces aholistic analytical framework built upon the formal method of stochasticoptimization. We propose a robust algorithm which provides a streaming ratesuch that the video quality degrades below that rate with very low probabilityeven in presence of uncertain head movement, and bandwidth. It assumes theknowledge of the viewing probability of different portions (tiles) of apanoramic scene. Such probabilities can be easily derived from crowdsourcedmeasurements performed by 360 video content providers. We then proposeefficient methods to solve the problem at runtime while achieving a boundedoptimality gap (in terms of the QoE). We implemented our proposed approachesusing emulation. Using real users' head movement traces and real cellularbandwidth traces, we show that our algorithms significantly outperform thebaseline algorithms by at least in $30\%$ in the QoE metric. Our algorithmgives a streaming rate which is $50\%$ higher compared to the baselinealgorithms when the prediction error is high.

A Robust Algorithm for Tile-based 360-degree Video Streaming with Uncertain FoV Estimation