## 2 Related Work
They addressed the major issue that the users can perceive from the multimedia streaming, which is the QoE, along with the challenges that influence the QoS just like the variation of channel condition, which is normally ignored by the HAS technologies. As the number of users increases, it is hard to satisfy their needs in terms of throughput. This is why authors in [14] proposed a Software Defined Network (SDN)-based Transmission Control Protocol (TCP) throughput management algorithm to provide fairness to competing users over a wireless network. The main ideas are to avoid oscillation of the video quality and stable buffer length. Authors showed significant improvement of fairness among users. A playback length changeable 3D video data chunk segmentation (PLC3 DCS) algorithm and a hybrid-priority based 3D video P2P data scheduling algorithm were presented for heterogeneous networks. Bandwidth-adaptive 3D video P2P streaming was applied to gain better error-resilience and network utilization performance with improving the chunks' success delivery rates [15]. A high-quality video streaming technique was proposed [16]. The presented technique provides remotely formed content to be gained and streamed to multi-tile display environments on a heterogeneous wide area network. To minimize the entropy and needed bandwidth of the video stream, video compression was used. Thus, 1080p resolution and multimedia rich content with bandwidth requirements below 10 Mbps was delivered. A QoS supporting video streaming system over heterogeneous wireless networks was presented and the system was tested on real Wireless Local Area Network(WLAN) and LTE networks [17]. Fountain code was utilized to decrease errors and losses on unstable wireless network with consolidating many physical paths in a cost-effective way. An adaptive multi-interface selection video streaming system was proposed with a prototype that was improved on iOS (iPhone 5s) over LTE and WiFi network in [18]. Basically, the system was composed of a reliable cloud-based video delivery scheme together with the split-layer SVC encoding and real-time adaptive multi-interface selection. The system was tested in various real-world networks about improving the video quality. Video streaming to mobile devices is an example of content-on-the-move, which has gained a great increase in volume together with multimedia traffic in recent years. Thus, research on video streaming presents remarkable progress. In 2013, 53% of the global mobile Internet traffic was composed of video content delivery. Furthermore, by 2018, it is expected to reach 67% of the global mobile Internet traffic [19]. A distributed and adaptive resource management controller that allows the optimal exploitation of Cognitive Radio and soft-input/soft-output data fusion in Vehicular Access Networks has been designed and the problem of resource management through the use of stochastic network utility maximization for reducing the energy of the vehicular nodes through offloading traffic towards the cloud has been solved [20]. For variable available transmission capacities presenting multicasting video, SVC technology was proposed as a remarkable mechanism. For real-time dissemination of video, wireless video multicasting was proposed as an effective solution. Wireless video multicasting utilizes its own broadcast structure and SVC is a good solution for the heterogeneity problem of networks. One base layer and many higher layers form the SVC-encoded video stream. Video's main quality is given by the base layer. More than one higher layer presents identical video with rising qualities. On the transmitting side, variable Modulation and Coding Schemes (MCSs) are implemented on variable video layers. Thus, a subset of layers is received that is available for channel conditions [21]. Lately, high-definition video data utilization in cellular networks has risen because of the ever increasing high-end smart phone usage.