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Scalable Video Coding (SVC) has been widely used in video transmissions. However, inappropriate SVC structures may lead to received video quality lower than user’s requirement or resource waste, especially in underwater time-varying channels. In this work, an adaptive cross-layering solution is proposed and validated for video transmissions in underwater acoustic multicast networks, namely Adaptive Scalable Video Transmission (ASVTuw). In ASVTuw, the transmitter collects over time the information about the channel states and the users’ video quality requirements to adaptively select the SVC video structures and transmission schemes, using Machine Learning (ML). At-sea experiments were conducted to collect the required acoustic data. The collected data were then used in MATLAB simulations to validate the ASVTuw. The results show that the usage of ASVTuw avoids resource wasting from transmitting redundant SVC substreams and satisfies the multicast users’ video quality requirements effectively with higher flexibility compared with the existing noncross-layering designs. 

Achieving high throughput and reliability in underwater acoustic networks is a challenging task due to the bandwidth-limited and unpredictable nature of the channel. In a multi-node structure, such as in the Internet of Underwater Things (IoUT), the efficiency of links varies dynamically because of the channel variations. When the channel is not in good condition, e.g., when in deep fade, channel-coding techniques fail to deliver the required information even with multiple rounds of retransmissions. An efficient and agile collaborative strategy among the nodes is required to assign appropriate resources to each link based on their status and capability. Hence, a cross-layer collaborative strategy is introduced to increase the throughput of the network by allocating unequal share of system resources to different nodes/links. The proposed solution adjusts the physical- and link-layer parameters in a collaborative manner for a Code Division Multiple Access (CDMA)-based underwater network. An adaptive Hybrid Automatic Repeat Request (HARQ) solution is employed to guarantee reliable communications against errors in poor communication links. Results are being validated using data collected from the LOON underwater testbed, which is hosted by the NATO STO Centre for Maritime Research and Experimentation (CMRE) in La Spezia, Italy.