Quality-aware adaptive delivery of multi-view video

Advances in video coding and networking technologies have paved the way for the Multi-View Video (MVV) streaming. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D viewing experience may be degraded signifi- cantly, unless quality-aware adaptation methods are deployed. There is no research work to discuss the MVV adaptation of decision strategy or provide a detailed analysis of a dynamic network environment. This work addresses the mentioned issues for MVV streaming over HTTP for emerging multi-view displays. In this research work, the effect of various adaptations of decision strategies are evaluated and, as a result, a new quality-aware adaptation method is designed. The proposed method is benefiting from layer based video coding in such a way that high Quality of Experience (QoE) is maintained in a cost-effective manner. The conducted experimental results on MVV streaming using the proposed strategy are showing that the perceptual 3D video quality, under adverse network conditions, is enhanced significantly as a result of the proposed quality-aware adaptation

Quality-aware adaptive delivery of multi-view video

Advances in video coding and networking technologies have paved the way for the Multi-View Video (MVV) streaming. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D viewing experience may be degraded signifi- cantly, unless quality-aware adaptation methods are deployed. There is no research work to discuss the MVV adaptation of decision strategy or provide a detailed analysis of a dynamic network environment. This work addresses the mentioned issues for MVV streaming over HTTP for emerging multi-view displays. In this research work, the effect of various adaptations of decision strategies are evaluated and, as a result, a new quality-aware adaptation method is designed. The proposed method is benefiting from layer based video coding in such a way that high Quality of Experience (QoE) is maintained in a cost-effective manner. The conducted experimental results on MVV streaming using the proposed strategy are showing that the perceptual 3D video quality, under adverse network conditions, is enhanced significantly as a result of the proposed quality-aware adaptation

Adaptive delivery of immersive 3D multi-view video over the Internet

The increase in Internet bandwidth and the developments in 3D video technology have paved the way for the delivery of 3D Multi-View Video (MVV) over the Internet. However, large amounts of data and dynamic network conditions result in frequent network congestion, which may prevent video packets from being delivered on time. As a consequence, the 3D video experience may well be degraded unless content-aware precautionary mechanisms and adaptation methods are deployed. In this work, a novel adaptive MVV streaming method is introduced which addresses the future generation 3D immersive MVV experiences with multi-view displays. When the user experiences network congestion, making it necessary to perform adaptation, the rate-distortion optimum set of views that are pre-determined by the server, are truncated from the delivered MVV streams. In order to maintain high Quality of Experience (QoE) service during the frequent network congestion, the proposed method involves the calculation of low-overhead additional metadata that is delivered to the client. The proposed adaptive 3D MVV streaming solution is tested using the MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) standard. Both extensive objective and subjective evaluations are presented, showing that the proposed method provides significant quality enhancement under the adverse network conditions

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

Objective assessment of region of interest-aware adaptive multimedia streaming quality

Adaptive multimedia streaming relies on controlled adjustment of content bitrate and consequent video quality variation in order to meet the bandwidth constraints of the communication link used for content delivery to the end-user. The values of the easy to measure network-related Quality of Service metrics have no direct relationship with the way moving images are perceived by the human viewer. Consequently variations in the video stream bitrate are not clearly linked to similar variation in the user perceived quality. This is especially true if some human visual system-based adaptation techniques are employed. As research has shown, there are certain image regions in each frame of a video sequence on which the users are more interested than in the others. This paper presents the Region of Interest-based Adaptive Scheme (ROIAS) which adjusts differently the regions within each frame of the streamed multimedia content based on the user interest in them. ROIAS is presented and discussed in terms of the adjustment algorithms employed and their impact on the human perceived video quality. Comparisons with existing approaches, including a constant quality adaptation scheme across the whole frame area, are performed employing two objective metrics which estimate user perceived video quality