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The results of our simulation and the subjective test show that our proposed rate control system for 3D multi-view video allows for the transmission of different bitstreams at a higher quality, compared to non-dynamic adaptive rate control, given network bandwidth fluctuations We also provide a user-based test for subjective evaluation of the rendered views, to decide on the number of views and quality of each view to be encoded for a specific network condition. The proposed system is tested under different network conditions.

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We show how to prepare the content at the server side and present a policy for the client to choose content from the server based on our distortion model for views reconstruction. Our rate control system has been built on top of two state-of-the-art key technologies: High Efficiency Video coding (HEVC), and MPEG’s Dynamic Adaptive Streaming over HTTP (DASH). In this paper, we propose a dynamic adaptive rate control system and its associated rate-distortion model for multi-view 3D video transmission, which will improve the user’s quality of experience in the face of varying network bandwidth. The reduction rates of average file size and average blocking artifact were about 4.9% and 8.3% over maximum bitrate encoding, respectively. We validated our method throughout multiple types of videos, showing improved visual quality for the same file size based on commonly used quality assessment measures, such as MSU blocking, MSU blurring, SSIM, 3SSIM, and stSSIM. Finally, we define and optimize our objective function from the blocking artifact density functions in order to select a bitrate with minimum perceptual blockiness and file size for each frame. Based on the impact metric, we generate a blocking artifact density functions for the available bitrates, on the whole video. The proposed method consists of the following steps: First, we define a simple and fast impact metric in order to identify the blocking artifacts in each frame of multiple videos, encoded at different bitrates. We propose a new direction of constructing mixed bitrate video based on content-based image analysis on each video frame, which was posed as a problem of pre-analysis for the final video encoding step. The trade-off between these artifacts and bi-trate can be improved by adaptively selecting frames from a set of video copies encoded at different bitrates, prior to actual video encoding.

Our evaluation using the real player implementation shows that BETA improves video quality by up to 20%, reduces number of stalls by 20%-100% in nearly 80% of cases, and cuts down wasted bandwidth by 22%-100%.īlocking artifacts, commonly introduced during video encoding, are one of the major causes of reduced perceptual video quality. BETA can work with any adaptation algorithm or HAS player to significantly improve robustness and efficiency in dynamic network environments and for low-latency streams, as well as to dramatically reduce content storage and encoding infrastructure requirements. We define a new HAS-oriented transmission order of video frames within segments that facilitates decodability of partial frames and paves the way for changing the paradigm from discrete to continuous bitrate ladders for HAS. We propose BETA- Bandwidth-Efficient Temporal Adaptation, an agile approach that allows HAS players to refine the quality level within video segments on the fly, according to the actual bandwidth conditions experienced while downloading each segment. This approach is not robust to bandwidth fluctuation at small time scales, which can consequently lead to stalls, bandwidth waste, and unstable quality, mainly due to the inability to mitigate significant bandwidth reduction during the segment download. Conventional approach to adaptation is to make a decision on the next video segment quality based on hysteresis of prior throughput measurements. However, effective adaptation that minimizes stalls and start-up time while maximizing quality and stability remains elusive, especially when available bandwidth is variable or multiple players compete for the bottleneck capacity. To cope with diverse network conditions, HTTP Adaptive Streaming (HAS) enables video players to dynamically change the video quality throughout the video stream.

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