## 1 Introduction  
The advances in Micro Electro-Mechanical Systems (MEMS) and wireless communications resulted in the wireless sensor networks (WSN). These networks are comprised of large numbers of low-cost, low-power and multifunctional sensor nodes. Thus, it is predicted that wireless sensor networks will become conventional in our daily life and recently there have been a lot of hot research topics in this field of study [1]. Nowadays, a single sensor device can be equipped with audio and visual information collection modules using low-cost hardware such as CMOS cameras, array sensors and microphones. This fostered the development of Wireless Multimedia Sensor Networks (WMSN), in a way that they are able ubiquitously to obtain multimedia content such as video and audio streams, still images, and scalar sensor data from the environment [2]. Currently wireless sensor networks are used widely in multimedia streaming. Multimedia surveillance sensor networks [3], advanced health care delivery [4], automated assistance for the elderly and family monitors [5], traffic avoidance, enforcement and control systems [6], and industrial process control [7] are all instances of new WMSN applications. Real-time multimedia streaming is used by some applications such as emergency response, video surveillance systems, battlefield, disaster discovery and indoor security, to name but a few. End-to-end delay and loss should be identified for multimedia during network transport [8]. Most of these applications use WMSN with video sensor nodes (VSN), which are called wireless video-based sensor networks (WVSN). WVSNs were initially devised as a collection of small, inexpensive, battery operated nodes with the ability to communicate with each other wirelessly over a limited transmission range. These networks are different from traditional wireless sensor networks due to the fact that nodes are equipped with very low power cameras. These camera-nodes have the ability to capture visual information of observed areas at variable rates, process the data on-board and transmit the captured data through the multi-hop communication to the base-station (Sink) [9]. Generally, the two most important challenges for these systems are energy-efficiency and video Quality of Service. In other words, the main problem is how to simultaneously provide energy efficiency and video quality in WVSN. The huge amount of data generation and transmission by VSNs causes them to consume a lot of energy. Therefore, the limited power supply in sensor nodes becomes the bottleneck in transmitting multimedia in WVSNs [10]. On the other hand, video quality suffers from the limited power, processor, memory and radio frequency of VSNs. Most of the previous works are devoted to image transmission [11-14] while the research on video transmission over WSN is still in the earlier stages. Therefore, in this article a new architecture for WVSN to transmit video streams, called Energy-efficient and high-Quality Video transmission Architecture (EQV-Architecture) is presented. This architecture is designed with the objectives of extending the lifetime of VSNs and increasing QoS. Thus, EQV-Architecture prolongs lifetime of VSN while preserving video quality. Literature surveys show that most of previous works considered only one of these criteria. The application, transport, and network layers of the communication stack are customized for video transmission in EQV-Architecture to improve the performance of WVSN. In the customization procedure a new sub-application layer protocol is presented with innovative compression andprioritization algorithms. To provide suitable service for application layer it is necessary to propose new transport and network layer protocols. In the proposed transport protocol, packets are sent as bursts and inter-layer command messages are used in order that it can be aware of the status of network. Data retransmission is omitted to achieve real-time communication.