Highly efficient distribution and management of media resources and fast content discovery are key determinants for mobile peer-to-peer video-on-demand solutions, especially in wireless mobile networks. Virtual communities making use of users' common characteristics such as interest and interaction to describe the boundary of sharing content and objects are a promising avenue for high-efficiency resource sharing. In this paper, we propose a novel ant-inspired mini-community-based video sharing solution for on-demand streaming services in wireless mobile networks (AMCV). AMCV relies on a newly designed two layer architecture and on an algorithm inspired from the indirect communication between ants via pheromone trails which enables them to discover and use shortest paths. The architecture is composed of a mini-community network layer and a community member layer. The ant-inspired algorithm enables finding the common interest of users in video content within large amounts of pseudo disorderly interactive behavior data. AMCV proposes an ant colony optimization-based community communication strategy that dynamically bridges communities to support fast search for resources. AMCV achieves high scalability by making use of a designed community maintenance mechanism to uniformly distribute the maintenance cost of members and resources in the community, according to various member roles. Simulation-based testing shows how AMCV outperforms another state-of- the-art solution in terms of a wide set of performance metrics.

Cooperatively fetching video content with the help of other mobile nodes can release some of the pressure on storage and bandwidth of the constrained mobile devices in wireless networks and speeds up the localization process of video resources so as to support better quality of viewing experience. In this context, the discovery of the appropriate mobile cooperative node becomes a key factor and is also a challenge for the deployment of such a fetching scheme. In this paper, we introduce a novel cooperative content fetching-based strategy to increase the quality of video delivery to mobile users in wireless networks (CCF). By intelligently monitoring the real-time variation in the state of the one-hop neighbors (immediate-neighbors) of the video resource downloader, CCF employs an innovative estimation model to measure the stability of these immediate-neighbors. In order to enhance the cooperative fetching efficiency, CCF designs a communication quality forecast model to measure link reliability and forecast the available bandwidth. By making use of a newly proposed cooperative fetching algorithm, CCF can speed up fetching and disseminating of video resources with the help of cooperative neighbors selected in terms of stability and communication quality. Simulation results show how CCF obtains higher selection accuracy of cooperative neighbors, lower average end-to-end delay, lower average packet loss ratio, higher average throughput, higher video quality, and lower maintenance overhead in comparison with state-of-the-art solutions.

The huge amounts of network traffic generated by the ultra-high-definition video playback of super-large-scale video users results in tremendous pressure for front-haul and back-haul in the 5G network, which brings severely negative influence for the large-scale deployment and scalability of video systems and video delivery performance (e.g. transmission delay and packet loss) related to user quality of experience. The direct D2D communications between mobile devices with adjacent position in geographical area can offloading huge video traffic into the underlying networks, which reduces load of cellular base station in the edge networks and relieves traffic pressure in the core networks. In this paper, we propose a novel Social-Aware D2D Video Delivery Method based on Rapid Sample-Efficient Measurement of Mobility Similarity in 5G Ultra-Dense Network (DMSEM). By investigation for one-hop D2D pair relationship, DMSEM builds a social state transition model of user movement, which makes use of encounter duration and shared video length between encountered users to define the state transition condition. A cluster algorithm of encounter events is proposed, which achieves initial clusters of encounter events by calculating similarity between encounter events from the two aspects of both variation rate of geographical distance between mobile users and encounter duration time. DMSEM makes use of the Fuzzy C-Means to refine the initial clusters and extracts encounter patterns of mobile users. DMSEM designs a sample-efficiency rapid recognition algorithm of encounter pattern, which can use small number of encounter distance samples to achieve fast heuristic recognition of encounter pattern. Extensive tests show how DMSEM achieves better results in comparison with other state-of-the-art solutions in terms of packet loss rate, average freeze time, cache utilization, average bitrate, buffer level and control overhead.

The video traffic offloading in edge networks is an effective method for remission of congestion of backward paths in 5G networks by continual optimization of video distribution to promote scale and efficiency of video delivery in edge networks (e.g., D2D-based near-end sharing). Because the video resources are dispersedly cached in local buffer of mobile devices of video users, the management of local video resources of video users in edge networks (e.g., caching and removing of local videos) causes dynamic variation of video distribution in networks. The real-time adjustment of local resources of users in terms of the influence levels (e.g., promotion and recession) of video sharing performance is significant for the continual distribution optimization. In this paper, we propose a novel Social-aware Edge Caching Strategy of Video Resources in 5G Ultra-Dense Network (SECS). SECS designs an estimation method of interest domain of users, which employs the Spectral Clustering to generate initial video clusters and makes use of the Fuzzy C-Means (FCM) to refine the initial video clusters. A user clustering method is proposed, which enables the users with common and similar interests to be clustered into the same groups by estimating similarity levels of interest domain between users. SECS designs a performance-aware video caching strategy, which enables the users intelligently implement management (caching and removing) of local video resources in terms of influence for the intragroup sharing performance. Extensive tests show how SECS achieves much better performance results in comparison with the state-of-the-art solutions.

Accurate video launching and propagation is significant for promotion and distribution of videos. In this paper, we propose a novel video propagation strategy that fuses user interests and social influences based on the assistance of key nodes in social networks (VPII). VPII constructs an estimation model for video distribution capacities in the process of video propagation by investigating interest preference and influence of social users: (1) An estimation method of user preferences for video content is designed by integrating a comparative analysis between current popular videos and historical popular videos. (2) An estimation method to determine the distribution capacities of videos is designed according to scale and importance of neighbor nodes covered. VPII further designs a multi-round video propagation strategy with the assistance of the selected key nodes, which enables these nodes to implement accurate video launching by estimating weighted levels based on available bandwidth and node degree centrality. The video propagation can effectively promote the scale and speed of video sharing and efficiently utilize network resources. Simulations-based testing shows how VPII outperforms other state-of-the-art solutions in terms of startup delay, caching hit ratio, caching cost and higher control overhead.