Cihat Cetinkaya

SDN for segment based flow routing of DASH

In this paper we propose an SDN based dynamic path selection for HTTP-based video streaming. MPEG-DASH is a recently proposed standard allowing rate adaptation over HTTP. On the other hand, Software Defined Networking (SDN) is a new network architecture, which allows determining routes of packet flows by an external controller software. In this study we develop an optimization model aiming to obtain maximum throughput for DASH services by selecting the optimal paths for video packet flows over SDN. The simulations show that the clients in the proposed system receive better QoE in terms of video bitrate, outage duration and startup delay when compared to the clients running in Internets best effort.

An SDN-assisted system design for improving performance of SVC-DASH

Today, the most of the video streaming system provides quality adaptation and prefers to send their packets over HTTP. MPEG group has standardized Dynamic Adaptive HTTP Streaming (DASH) regarding this tendency on the adaptive HTTP streaming. Besides providing quality adaptation with a non-scalable codec, DASH standard also allows Scalable Video Coding (SVC) to adapt quality. Software Defined Networks (SDN) is a recently emerged networking paradigm. SDN enables to separate control and data plane of computer networks and hence provides flexibility to network operators to implement their own routing approaches. In this paper, we propose a system for increasing Quality of Experience (QoE) of SVC-DASH clients by utilizing SDN. Our experimental results show that the proposed system provides an increase in received video quality and decrease in outage duration and startup delay when compared to the performance of the client running over todays Internet implementing shortest path routing.

Learning-based approach for layered adaptive video streaming over SDN

Software-defined networking is a recently emerging paradigm that decouples the control and data planes of computer networks. It allows for the implementation of application-specific routing algorithms, and the advantages that the SDN architecture enables can be used to enhance the performance of multimedia communication applications. In this paper, we propose an adaptive video streaming system with a learning-based approach, running over SDN. In the proposed video streaming system, we use a novel learning model to determine the optimal time to re-route the traffic flows and to change the bitrate of the video. The learning model aims to minimize the packet loss rate, quality changes and controller cost while adapting the flow routes and video quality. We have tested the performance of the learning-based approach by comparing it to traditional Internet routing and the greedy approach. The results show that the proposed system significantly outperforms the traditional Internet routing approach and the greedy approach in terms of quality of experience (QoE) and network cost under different network scenarios.

Parent Selection via Reinforcement Learning in Mesh-Based P2P Video Streaming

There are several successful deployments of peer to peer (P2P) video streaming systems which provide acceptable QoS. Researches on these systems continue to improve the experienced quality by system users. Since received video quality mostly depends on the parent selection, an efficient parent selection algorithm can increase the received video bitrate by peers and provide seamless streaming. In this paper, we propose a novel parent selection method based on reinforcement learning. By the proposed system model, the newly joined peer explores the peers in the system first, and uses this information for its further parent selection actions. We implemented our model on a Cool Streaming-like P2P video streaming system in ns3. Our results indicate that, selected parents by using reinforcement learning approach improve the playback continuity, with respect to parent selection method used by Cool Streaming. Furthermore, reinforcement learning approach helps peers to find more stable parents in case of peer churn.

Video-on-demand system architecture with ALTO-SDN integration

Software Defined Networking (SDN) is a promising technology for future internet applications through decoupling the data and control planes of computer networks. Enabling the development of application specific routing algorithms, the separation fosters the performance of internet applications. On the other hand, Application Layer Traffic Optimization (ALTO) provides network related information such as network paths and geographic locations of hosts to applications running on the internet. Thus, it is possible to maintain an increase in the performance of the end-to-end internet applications by integrating SDN technology and ALTO protocol. In this paper, we propose a Video-on-Demand (VoD) architecture utilizing the integration of SDN and ALTO. In the architecture, the clients running the VoD applications reside in an SDN domain while the servers remain out of the SDN domain. Simulation results show that the integration of SDN and ALTO increases the Quality of Experience (QoE) in a VoD system compared to QoE which is achieved in a traditional VoD architecture.

Vascular landmark classification in retinal images using fuzzy RBF

This paper suggests the use of radial based neural networks for classification of the landmark points from retina vessels in the retinal vascular images to diagnose the disease in the diabetic retinopathy patients and to track the periodic differences in retinal vessel images. In the suggested method, Gold Standard images from DRIVE database are used. The performance of landmark detection by the suggested method shows that the method can be used as an algorithm for registration of retinal images.

ALTO-assisted CDN-based video streaming over SDN

Software-defined Networking (SDN) is a promising network architecture for future internet applications by seperating the control and data layer of computer networks. On the other hand, Application Layer Traffic Optimization (ALTO) protocol provides ISP-friendly communication by provisioning network related information to the applications. In this paper, we propose a method for selecting appropriate video server using SDN and ALTO in content distribution network (CDN) based video streaming systems. Simulation results show that with the proposed method, the quality of experience received by the clients in video streaming applications is increased and the communication cost between internet service providers is reduced.

Towards QoS-aware routing for DASH utilizing MPTCP over SDN

Multipath TCP (MPTCP) has gained great attention by the researchers and network application developers due to its features providing better bandwidth utilization and higher reliability recently. Utilizing MPTCP in the datacenters provides performance gain to the applications. If the underlying network has Software Defined Networking (SDN) architecture, the routing of the MPTCP subflows can be specialized. In this paper, we propose an architecture utilizing MPTCP in an SDN-based ISP in order to serve guaranteed services to the video streaming clients. The number of MPTCP subflows and routes of the subflows are determined based on a mixed integer linear programming model. The simulation results show that the proposed architecture provides increase in received throughput and decrease in duration of outages when it is compared to a general-purposed throughput maximization approach.

Server selection for video streaming applications over Software Defined Networks

Software Defined Networks (SDN) is a new technology providing a programmable network by separating control and data planes of computer networks. SDN technology can provide an increase in the performance of Internet applications by implementing routing approaches specific to such applications. In this paper, we propose a server and end-to-end path selection approach for a content distribution network (CDN) based video streaming system. Simulation results show that the proposed approach can increase the performance of CDN based video streaming systems.

Performance evaluation of DASH rate adaptation algorithms

MPEG group has recently proposed Dynamic Adaptive HTTP Streaming (DASH) standard. Various rate adaptation algorithms were proposed in the literature in order to maximize the Quality of Experience (QoE) in DASH clients. In this paper, the performances of pioneer DASH clients, which implement rate adaptation algorithm by taking the bandwidth measurements, buffer level, and both parameters into account, are examined. For this purpose, QoE parameters according to the different buffer sizes are observed under variable network conditions in a simulation environment. The results enlighten that which type of approach in which circumstances can be used in order to obtain best performance in terms of QoE.