Novel Strategies to Improve the Effectiveness of Field of View - Aware Edge Caching for Adaptive 360° Video Streaming




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Virtual Reality (VR) and 360° Video Streaming have attained a lot of popularity recently. Streaming 360° video to Head Mounted Displays (HMDs) over the internet is extremely demanding owing to its huge size, desirability to be viewed at higher resolutions, high bandwidth, and low latency requirements. However, viewers can view only a small portion of a scene in the video at a time, since viewers are limited by the Field of View (FoV) of the HMD. A few solutions use adaptive 360° video streaming by streaming high resolution video of only the part on the video in the viewers FoV, and low-resolution video for the part of the video that is not in the viewers FoV. FoV Adaptive 360° video streaming has been instrumental in decreasing the bandwidth requirements, but network latency is another factor that adversely affects the streaming of 360° videos from distant content servers. To overcome this, some solutions use caching of popular content at the mobile edge cloud server close to the end user. This caching policy helps reduce latency in the network and alleviate network bandwidth demands by decreasing the number of future requests that must be sent to the content server, thus reducing the load on the server. But most of these strategies use generic heat maps to determine popular content in videos among users. A viewers’ FoV is a depiction of that viewers’ area of interest in the video at any point in time - with the center of the FoV being of utmost importance grabbing the viewers’ attention and the peripheries of the FoV of relatively lesser importance, importance decrease as we move from the center to the periphery in the FoV. Anything outside the FoV of the viewer is of no importance since the user chose not to see that part in the video. The importance of a part in the video portrays its popularity among users – the more the importance, the more the popularity. The popularity of different parts of the video based on the past viewers’ viewing history determines how significant each part of the video is to be cached at the edge servers in the above-mentioned caching policy. More the popularity, the more the probability of the content to be cached. However, the use of traditional heat maps to determine the popularity of video content gives equal importance to the entire FoV and fails to cater to the requirement of declining importance given to different parts of the FoV as we move farther away from the center of the FoV. In this thesis, we show how the use of such heatmaps gives wrong impression of the popularity of video contents -contents that are not so popular appear to be popular. This false notion created by heat maps renders them useful only in highly constricted cases. We demonstrate the relevance of some indices used in election analysis to overcome this limitation in heat maps and discuss where they would work best. We also introduce the concept of Vote decay in the popularity of contents in the FoV to remove misinterpretations of content importance so that we can improve caching decisions and future FoV predictions.



Virtual reality, Streaming video, Virtual reality headsets, Web servers