A Novel Video Transmission Optimization Mechanism Based on Reinforcement Learning and Edge Computing

As we know, the video transmission traffic already constitutes 60% of Internet downlink traffic. The optimization of video transmission efficiency has become an important challenge in the network. This paper designs a video transmission optimization strategy that takes reinforcement learning and edge computing (TORE) to improve the video transmission efficiency and quality of experience. Specifically, first, we design the popularity prediction model for video requests based on the RL (reinforcement learning) and introduce the adaptive video encoding method for optimizing the efficiency of computing resource distribution. Second, we design a video caching strategy, which adopts EC (edge computing) to reduce the redundant video transmission. Last, simulations are conducted, and the experimental results fully demonstrate the improvement of video quality and response time.

Blockchain-Enabled Intelligent Video Caching and Transcoding in Clustered MEC Networks

In recent years, the number of smart devices has exploded, leading to an unprecedented increase in demand for video live and video-on-demand (VoD) services. Also, the privacy of video providers and requesters and the security of requested video data are much more threatened. In order to solve these issues, in this paper, a blockchain-enabled CMEC video transmission model (Bl-CMEC) for intelligent video caching and transcoding will be proposed to ensure the transactions’ transparency, system security, user information privacy, and integrity of the video data, enhance the ability of severs in actively caching popular video content in the CMEC system, and realize transcoding function at network edge nodes. Furthermore, we chose a scheme based on deep reinforcement learning (DRL) to intelligently access the intracluster joint caching and transcoding decisions. Then, the joint video caching and transcoding decision smart contract is specially designed to automatically manage the transaction process of the joint caching and transcoding service, which records key information of joint caching and transcoding transactions and payment information on a continuous blockchain. The simulation results demonstrate that the proposed Bl-CMEC framework not only can provide users with better QoE performance for video streaming service but also can ensure the security, integrity, and consistency for the video providers, video requesters, and video data.

Social-Aware Edge Caching Strategy of Video Resources in 5G Ultra-Dense Network

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.