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On Data Wastage in Viewport-Dependent Streaming

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Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)978-1-7281-1817-8
ISBN (painettu)978-1-7281-1818-5
DOI - pysyväislinkit
TilaJulkaistu - syyskuuta 2019
Julkaistu ulkoisestiKyllä
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Workshop on Multimedia Signal Processing -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

NimiIEEE International Workshop on Multimedia Signal Processing
ISSN (painettu)2163-3517
ISSN (elektroninen)2473-3628

Conference

ConferenceIEEE International Workshop on Multimedia Signal Processing
Ajanjakso1/01/00 → …

Tiivistelmä

Omnidirectional videos are becoming more and more popular as display devices, such as Head Mounted Displays (HMD), become more user friendly. One of the biggest challenges with such video content is the great amount of bandwidth required for delivery at a good quality. The key difference between omnidirectional and the traditional content streaming is that with the former, users watch only part of the streamed content. Therefore, the non-watched areas of a streamed omnidirectional video are delivered unnecessarily. To reduce the network bandwidth requirements, the delivery mechanism could make use of Viewport-Dependent Streaming (VDS), according to which high-quality (HQ) video is maintained only in the current viewport, while lower quality video is streamed for the remaining part. In this paper, we examine the streaming data wastage, i.e., the parts of HQ video not watched by a user, in VDS when users linearly move from a generic viewport to another one within the 360 degrees space. We used both simulated trajectories as well real trajectories from 20 users. We show how the user's HMD exploration speed and the DASH transport segment lengths impact the streaming data wastage, leading up to 78% of wasted HQ content and up to 40% of total stream bit rate wasted due to users' motion. We also show how the users' trajectory prediction can decrease the streaming data wastage making the system up to 25% more efficient. This paper gives very useful hints that could lead to VDS optimizations, and it introduces quantitative estimates on how the data wastage characteristics change with respect to the streaming parameters.

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