Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Multisensory 360 videos under varying resolution levels enhance presence

Omnidirectional videos have become a leading multimedia format for Virtual Reality applications. While live 360◦ videos offer a unique immersive experience, streaming of omnidirectional content at high resolutions is not always feasible in bandwidth-limited networks. While in the case of flat videos, scaling to lower resolutions works well, 360◦ video quality is seriously degraded because of the viewing distances involved in head-mounted displays. Hence, in this paper, we investigate first how quality degradation impacts the sense of presence in immersive Virtual Reality applications. Then, we are pushing the boundaries of 360◦ technology through the enhancement with multisensory stimuli. 48 participants experimented both 360◦ scenarios (with and without multisensory content), while they were divided randomly between four conditions characterised by different encoding qualities (HD, FullHD, 2.5K, 4K). The results showed that presence is not mediated by streaming at a higher bitrate. The trend we identified revealed however that presence is positively and significantly impacted by the enhancement with multisensory content. This shows that multisensory technology is crucial in creating more immersive experiences

Joint Communication and Computational Resource Allocation for QoE-driven Point Cloud Video Streaming

Point cloud video is the most popular representation of hologram, which is the medium to precedent natural content in VR/AR/MR and is expected to be the next generation video. Point cloud video system provides users immersive viewing experience with six degrees of freedom and has wide applications in many fields such as online education, entertainment. To further enhance these applications, point cloud video streaming is in critical demand. The inherent challenges lie in the large size by the necessity of recording the three-dimensional coordinates besides color information, and the associated high computation complexity of encoding. To this end, this paper proposes a communication and computation resource allocation scheme for QoE-driven point cloud video streaming. In particular, we maximize system resource utilization by selecting different quantities, transmission forms and quality level tiles to maximize the quality of experience. Extensive simulations are conducted and the simulation results show the superior performance over the existing scheme

Dynamic Viewport-Adaptive Rendering in Distributed Interactive VR Streaming: Optimizing viewport resolution under latency and viewport orientation constraints

In streaming Virtual Reality to thin clients one of the main concerns is the massive bandwidth requirement of VR video. Additionally, streaming VR requires a low latency of less than 25ms to avoid cybersickness and provide a high Quality of Experience. Since a user is only viewing a portion of the VR content sphere at a time, researchers have leveraged this to increase the relative quality of the user viewport compared to peripheral areas. This way bandwidth can be saved, since the peripheral areas are streamed at a lower bitrate. In streaming $360\degree$ video this has resulted in the common strategy of tiling a video frame and delivering different quality tiles based on current available bandwidth and the user's viewport location. However, such an approach is not suitable for real-time Interactive VR streaming. Furthermore, streaming only the user's viewport results in the user observing unrendered or very low-quality areas at higher latency values. In order to provide a high viewport quality in Interactive VR, we propose the novel method of Dynamic Viewport-Adaptive Rendering. By rotating the frontal direction of the content sphere with the user gaze, we can dynamically render more or less of the peripheral area and thus increase the proportional resolution of the frontal direction in the video frame. We show that DVAR can successfully compensate for different system RTT values while offering a significantly higher viewport resolution than other implementations. We further discuss how DVAR can be easily extended by other optimization methods and discuss how we can incorporate head movement prediction to allow DVAR to optimally determine the amount of peripheral area to render, thus providing an optimal viewport resolution given the system constraints

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise