# A Framework for Symmetric-Quality S3D Video Streaming Services
## 3 Proposed Solution
This results in the generation of the right-view video enhancement layer stream, which provides high-resolution video. By following these steps, the proposed encoding framework effectively addresses the issue of asymmetry between the left and right videos while maintaining bandwidth efficiency and delivering high-resolution video quality. In particular, by combining VEI and Cross-View SHVC, the quality of the right-view video is improved to the same level as the left-view video, minimizing discomfort for viewers during binocular viewing. Additionally, depending on bandwidth conditions, there is the advantage of selectively providing right-view videos ranging from low to high quality. This allows for an optimized user experience by delivering the appropriate video quality based on network conditions or the performance of the user's device. Moreover, by reducing encoding complexity and improving efficiency, the framework optimizes resource usage for service providers and enhances the overall system performance. Figure 10 illustrates the overall structure of the decoding framework, visually representing the step-by-step process of decoding based on the streams generated using the proposed method. This figure systematically explains the flexible decoding process designed to adapt to various network environments and bandwidth conditions. The proposed encoding and decoding frameworks offer significant advantages by providing users with various options depending on network conditions and available bandwidth. Users can choose between high-quality 2D service and low-quality 2D service based on the current network environment. For 3D services, they can select between asymmetric quality and symmetric quality options. This optimizes the user experience, allowing for satisfactory content consumption depending on network performance and device capabilities. Notably, this decoding framework is distinguished by its ability to deliver high-quality services while minimizing bandwidth consumption through the efficient hierarchical coding method and the conditional replenishment algorithm. For instance, during the provision of 3D services, the framework addresses the quality asymmetry between left and right views by incorporating a process to restore the right view with high quality. These restored images are then used as high-quality predictive signals in the enhancement layer generation. Furthermore, the decoding process is designed to maintain service consistency even under deteriorating network conditions. For example, in situations where bandwidth is limited, lower-quality streams are selectively received and decoded. Conversely, when sufficient bandwidth is available, higher-quality streams are received to provide superior visual quality. This ensures that the proposed framework not only delivers personalized services but also efficiently utilizes network resources, contributing to the reduction in operational costs for service providers. In conclusion, the decoding framework presented in Figure 10 visually demonstrates the entire process of optimized content delivery that reflects user preferences and network conditions. It highlights the key technical strengths of the proposed methodology and its contributions to enhancing the user experience.