The rapidly increasing popularity of immersive multimedia services such as live holographic communication represents the future trend of extended reality (XR) applications. However, the realization of such immersive and interactive experiences is limited by the lack of fundamental understanding of how different user behaviours and environmental factors jointly affect the overall quality of experience (QoE). In particular, compared with the media adaptation mechanisms applied in conventional video applications, considerably more independent factors may influence user QoE in these applications, including both human- and network-related factors. In this paper, we investigate the fundamental design principles of dynamic media adaptation methods for live holographic communication by holistically considering these factors. Specifically, a machine learning-based scheme is introduced to facilitate intelligent adaptation of both the frame quality (resolution level) and frame rate according to specific contexts, such as the user intent/behaviour (including object motion patterns and user movements) and real-time network conditions. Extensive real-world experiments are conducted to assess the feasibility and performance of the proposed method, and comparisons with state-of-the-art methods are performed. The results indicate that the proposed approach can effectively satisfy user intent, with increased user QoE.

Driven by the rapid advancement of XR technologies, an increasing demand has emerged for enabling interactive holographic real-time applications of multiple users. However, in wireless systems, the challenges of practically delivering a satisfactory experience for such applications arise not only from the high bandwidth and computational demands of holographic content but also from the need to effectively integrate and transmit additional sensory data, such as haptic feedback, to convey accurate and human-perceivable semantics. In this paper, we propose an architecture that leverages the computational capabilities of 5G edge computing to collaborate with multi-user haptic-integrated holographic video applications. We detail the components of this solution architecture, including haptic-integrated holographic video capture and streaming, and a simple reinforcement learning algorithm to synchronise multiuser frames at the MEC server. A prototype of a representative hand-touch social interaction application was designed, implemented, and measured to exemplify the potential of human-perceivable, haptic-integrated holographic video applications.

As a representative application paradigm for supporting volumetric video, point cloud is able to capture three-dimensional spatial information of objects, offering dynamic and immersive experiences to end users. Compared to the conventional bit-to-bit communication principle, the design of semantic communication ingeniously utilizes joint source and channel coding to transmit semantic features instead. By taking into account the common content delivery requirements of low bandwidth consumptions and low latency, while maintaining high resolutions, we present in this paper a novel framework called Spatial-Temporal Semantic Point Cloud Transmission (ST-SPCT). Compared to the existing point cloud compression and semantic communication methods that extract and reconstruct features only in the spatial dimension, the ST-SPCT simultaneously extracts and reconstructs spatial-temporal semantic features more deeply, thus significantly reducing computational time and data volume, while ensuring negligible compromises in peak signal-to-noise ratio (PSNR), chamfer distance (CD) metrics, and frame continuity according to our systematic experiment results.

The high mobility feature of vehicular networks poses tremendous challenges to maintaining network connectivity. In this paper, we investigate the possibility of enhancing the connectivity of Cellular Vehicle-to-Everything (C-V2X) networks through distributed trajectory adjustment. Based on a physical layer abstraction model, we characterize the network connectivity enhancement problem as a network utility maximization and study its concavity. We propose a distributed trajectory updating algorithm that dynamically adjusts the trajectory of vehicles on top of their planned trajectory. The algorithm is distributed and requires only geo-location exchanges, which are readily available in V2X networks. Simulation results show that the mobility updating algorithm converges and improves the aggregated network utility by up to 48% compared to the scenarios without mobility tuning.