The Internet of Things (IoT) is evolving into the Internet of Video Things (IoVT) that supports massive smart devices and multiple video applications. However, how to effectively control massive devices and transmit large-volume video data have become challenges in the current IoVT. Inspired by device-to-device (D2D) communications and coalitional game, this article constructs a self-organized D2D collaborative video content sharing framework for the IoVT. Specifically, we first propose a collaboration mechanism by introducing the social attributes of IoVT devices and their owners. In this mechanism, D2D collaborative coalitions are automatically formed among IoVT devices and video content is shared in the coalitions through D2D links. In this way, the burden of controlling massive IoVT devices and video data traffic are offloaded. Then, by integrating the scalability of the scalable-high-efficiency-video-coding (SHVC) streams and the flexibility of D2D networking, a collaborative video streaming strategy is developed. It takes advantage of provider set arrangement and transmission scheduling to reduce the impact of network instability on video services. Simulation results verify the effectiveness of the proposed mechanism and strategy.

By offloading video streaming from the centralised cloud to the edge, mobile edge computing (MEC) servers offer new opportunities for real‐time video transmission. Deploying on the edge of users can ensure low latency transmission, however, the limited storage and computing ability cannot adapt to the currently used video transmission technologies such as video transcoding or simulcast. To solve this problem, a more flexible video transmission architecture needs to be considered. Under this motivation, the authors propose a device‐to‐device (D2D) assisted video streaming scheme, which fuses the technical advantages of MEC and scalable video coding. Specifically, they first construct a novel architecture for delay‐sensitive live video streaming services in edge‐enabled wireless heterogeneous networks named MEC‐enabled goodput‐aware (MEGA) model. Then they present a mathematical formulation for optimising the aggregation goodput performance of video traffic including both cellular and D2D links. Finally, they derive a three‐step solution based on a distributed heuristic algorithm. Numerical simulation results show that MEGA outperforms existing models in terms of goodput, end‐to‐end delay, effective loss rate, and users' quality‐of‐experience.

The advancement of the Internet of Things (IoT) has enabled a variety of innovative consumer electronics applications. In particular, multi-modal applications integrating audio, video, and haptic interactions have emerged, offering users more immersive experiences and enhanced remote interaction capabilities. However, the distributed and fragmented nature of IoT network resources presents significant challenges for ensuring haptic communications under strict constraints. To address these challenges, this paper proposes a joint coding and network resource scheduling solution for haptic communications in IoT environments. First, we design a scalable and flexible haptic coding scheme that supports efficient and seamless haptic streaming across heterogeneous IoT networks. Second, we introduce a deep learning-based imperceptible Quality of Experience (QoE) recognition method, which uses emotional immersion as an evaluation metric to achieve unobtrusive QoE monitoring. Finally, based on user QoE feedback, we develop an on-demand cooperative network resource scheduling strategy to ensure high QoE, reduce network resource wastage, and optimize the utilization of fragmented IoT resources. Simulation results validate the effectiveness of the proposed architecture and strategies.

Scalable Video Coding (SVC) streaming multicast is considered as a promising solution to cope with video traffic overload and multicast channel differences. To solve the challenge of delivering high‐definition SVC streaming over burst‐loss prone channels, we propose a social‐aware cooperative SVC streaming multicast scheme. The proposed scheme is the first attempt to enable D2D cooperation for SVC streaming multicast to conquer the burst‐loss, and one salient feature of it is that it takes fully into account the hierarchical encoding structure of SVC in scheduling cooperation. By using our scheme, users form groups to share video packets among each other to restore incomplete enhancement layers. Specifically, a cooperative group formation method is designed to stimulate effective cooperation, based on coalitional game theory; and an optimal D2D links scheduling scheme is devised to maximize the total decoded enhancement layers, based on potential game theory. Extensive simulations using real video traces corroborate that the proposed scheme leads to a significant gain on the received video quality.