Scalable video coding (SVC) and power division multiplexing (PDM) are crucial technologies for video streaming in internet-of-vehicle (IoV) networks due to their robustness over time-varying channels. However, the correlation of video tiers and channel fluctuations induce significant complexity in the joint design of SVC and PDM in IoV networks. To reduce such complexity, we propose a novel transmission strategy for SVC-based video streaming in PDM technology by facilitating cross-layer control across the application, data link, and physical layers. Specifically, we propose a concise framework where the modulation order, the power control, and prioritization of video tiers are jointly controlled. Moreover, we leverage the network abstraction layer units within an arbitrary group-of-pictures to improve the overall video data rate. The formulated joint control of the modulation order, the transmission power, and video-tier prioritization is proved to be a quasi-concave problem. Consequently, our proposed cross-layer optimization based SVC video transmission strategy could efficiently utilize the prioritized video tiers in the application layer, power control in the data-link layer, and modulation orders in the physical layer. Numerical results are used to demonstrate performance improvements over the benchmarks.

Recently, scalable video coding (SVC) has gained significant recognition in mobile video streaming because it can adapt bitstreams to time-varying transmission conditions. However, the coding performance of SVC, which is determined by its coding structure, has not been thoroughly studied. To address this issue, we propose analyzing the redundancy, reduction, distortion, and mutuality of video information within the video coding processes. This analysis facilitates the development of a novel information-theoretical framework for quantifying coding performance, which includes an information theory (IT)-based quantification method and a graphical representation system. The representation system accurately delineates the coding reference structure for encoding each video frame, while the proposed method utilizes mutual information to quantify the achievable coding performance of SVC under the delineated structure. To demonstrate the significance of our research, we apply the proposed framework to encode a basic coding unit, showcasing its effectiveness in improving SVC schemes. Consequently, our framework not only provides an efficient approach for quantifying the coding performance of SVC but also serves as an invaluable tool for optimizing SVC in various applications.