From Event: SPIE Optical Engineering + Applications, 2016
This paper proposes a layer based buffer aware rate adaptation design which is able to avoid abrupt video quality
fluctuation, reduce re-buffering latency and improve bandwidth utilization when compared to a conventional simulcast
based adaptive streaming system. The proposed adaptation design schedules DASH segment requests based on the
estimated bandwidth, dependencies among video layers and layer buffer fullness.
Scalable HEVC video coding is the latest state-of-art video coding technique that can alleviate various issues caused by
simulcast based adaptive video streaming. With scalable coded video streams, the video is encoded once into a number
of layers representing different qualities and/or resolutions: a base layer (BL) and one or more enhancement layers (EL),
each incrementally enhancing the quality of the lower layers. Such layer based coding structure allows fine granularity
rate adaptation for the video streaming applications.
Two video streaming use cases are presented in this paper. The first use case is to stream HD SHVC video over a
wireless network where available bandwidth varies, and the performance comparison between proposed layer-based
streaming approach and conventional simulcast streaming approach is provided. The second use case is to stream
4K/UHD SHVC video over a hybrid access network that consists of a 5G millimeter wave high-speed wireless link and a
conventional wired or WiFi network. The simulation results verify that the proposed layer based rate adaptation
approach is able to utilize the bandwidth more efficiently. As a result, a more consistent viewing experience with higher
quality video content and minimal video quality fluctuations can be presented to the user.
Srinivas Gudumasu, Ahmed Hamza, Eduardo Asbun, Yong He, and Yan Ye, "Layer-based buffer aware rate adaptation design for SHVC video streaming," Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99711M (Presented at SPIE Optical Engineering + Applications: August 31, 2016; Published: 28 September 2016); https://doi.org/10.1117/12.2235795.
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