Internet video ecosystems are faced with the increasing requirements in versatile applications, ubiquitous consumption and freedom of creation and sharing, in which the user experience for high-quality services has become more and more important. Internet is also under tremendous pressure due to the exponential growth in video consumption. Video providers have been using content delivery networks (CDNs) to deliver high-quality video services. However, the new features in video generation and consumption require CDN to address the scalability, quality of service and flexibility challenges. As a result, we need to rethink future CDN for sustainable video delivery. To this end, we give an overview for the Internet video ecosystem evolution. We survey the existing video delivery solutions from the perspective of economic relationships, algorithms, mechanisms and architectures. At the end of the article, we propose a data-driven information plane for video delivery network as the future direction and discuss two case studies to demonstrate its necessity.

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The Internet of Things (IoT) increases the number of connected devices and supports ever-growing complexity of applications. Owing to the constrained physical size, the IoT devices can significantly enhance computation capacity by offloading computation-intensive tasks to the resource-rich edge servers deployed at the base station (BS) via wireless networks. However, how to achieve optimal resource scheduling remains a challenge due to stochastic task arrivals, time-varying wireless channels and imperfect estimation of channel state information (CSI). In this paper, by virtue of the Lyapunov optimization technique, we propose the toward optimal resource scheduling algorithm under imperfect CSI (TORS) to optimize resource scheduling in an IoT environment. A convex transmit power and subchannel allocation problem in TORS is formulated. This problem is then solved via the Lagrangian dual decomposition method. We derive analytical bounds for the time-averaged system throughput and queue backlog. We show that TORS can arbitrarily approach the optimal system throughput by simply tuning an introduced control parameter β without prior knowledge of stochastic task arrivals and the CSI of wireless channels. Extensive simulation results confirm the theoretical analysis on the performance of TORS.

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Mobile video streaming is a successful example of Cyber-Physical-Social Systems (CPSS). How to schedule network resources and provide better mobile video streaming services for mobile users are very important. Scalable video streaming is regarded as a promising technology in wireless networks where the cognitive femtocells are overlaid within the coverage area of a macrocell network. In this paper, we study dynamic resource allocation for scalable video streaming over cache-enabled wireless networks with time-varying channel conditions. We formulate the scalable video streaming problem as a stochastic optimization problem which aims at maximizing the time-averaged system utility subject to the timeaveraged video cache constraint at the server and the cross-tier interference constraint on the primary user under the sparse deployment scenario of femtocells. By employing the Lyapunov optimization theory, we design a dynamic cache and resource allocation (DCRA) algorithm to solve this problem. Furthermore, the problem is decomposed into three subproblems, i.e., video layer selection, cache placement, and wireless resource allocation. Via solving these subproblems, we derive the video layer selection and cache placement strategies, and a wireless resource allocation algorithm to manage the cross-tier interference. Simulation results demonstrate the advantages of the proposed DCRA for streaming scalable video over time-varying wireless networks.

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