User Digital Twin-Driven Video Streaming for Customized Preferences and Adaptive Transcoding

Adaptive streaming technologies, such as MPEG-DASH and HLS, dynamically adjust the quality of a video stream based on current network conditions. While these standards have significantly improved the streaming experience, they typically rely on predetermined network parameters and do not account for individual user preferences or device capabilities. Recent research has focused on enhancing these models by incorporating more granular user data to predict and adapt to changes in user environment and content preferences in real-time.

The concept of user modeling involves creating detailed profiles that reflect individual preferences, viewing habits, and environmental factors. Traditional models often utilize static data, such as user surveys or historical viewing data, which do not effectively adapt to real-time changes in user behavior or context [8, 9]. Advances in machine learning have enabled more dynamic user models that can update themselves as new data becomes available, leading to more personalized and responsive media services.

Transcoding is the process of converting video content from one format to another, optimizing it for various playback conditions. Traditional transcoding techniques have focused on achieving a balance between compression efficiency and video quality [10, 11]. However, these methods often do not consider the specific needs of individual users. Recent developments in this area have introduced adaptive transcoding algorithms that leverage user context and device capabilities to optimize video streams specifically tailored to each viewer, thereby enhancing both resource utilization and user satisfaction.

Although the application of digital twins is well-established in industrial contexts, its integration into multimedia services is relatively novel. Digital twins in multimedia mimic the viewer’s context, preferences, and anticipated behaviors to offer a personalized viewing experience. By integrating these comprehensive digital replicas with adaptive streaming and transcoding technologies, it is possible to not only personalize content delivery but also optimize the operational aspects of streaming platforms, such as bandwidth management and data storage.

The reviewed literature highlights the ongoing efforts and significant advancements in the fields of adaptive video streaming, dynamic user modeling, and intelligent transcoding. However, the full potential of these technologies, particularly when integrated with user digital twins, remains largely untapped [12, 13]. This paper seeks to build on these foundations by proposing a framework that more effectively combines these elements to deliver a truly personalized and efficient streaming experience.

The architecture of our system consists of three main components: the User Digital Twin Module, the Adaptive Streaming Engine, and the Transcoding Optimization Engine. The User Digital Twin Module dynamically updates user profiles based on continuous input from user interactions and environmental sensors [14]. The Adaptive Streaming Engine uses these profiles to adapt video streaming parameters in real time. The Transcoding Optimization Engine adjusts transcoding settings to match the user’s current device and network conditions.

Data is collected from various sources, including direct user input, device sensors, and network performance metrics. The User Digital Twin Module processes this data to update user profiles using the following formula:

where $U_{new}$ is the updated user profile, $U_{old}$ is the previous profile, $U_{obs}$ represents the newly observed user data, and $\alpha$ is the learning rate, dictating how quickly the profile adapts to new data.

We employ several machine learning algorithms to predict user preferences and streaming quality requirements. For instance, a decision tree algorithm is used to classify user preference patterns based on historical data, while a neural network is utilized for predicting the optimal video quality settings under varying network conditions. These algorithms help in refining the user profile and informing the streaming and transcoding engines.

where $Q_{opt}$ is the optimal quality setting, $D$ represents device capabilities, $P$ is user preferences, and $N$ denotes current network conditions.

The Transcoding Optimization Engine adapts the video transcoding parameters based on the updated digital twin profile. This adaptation is guided by a set of rules derived from the machine learning models, ensuring that video quality is maximized without exceeding bandwidth constraints.

where $T_{params}$ are the transcoding parameters, $U$ is the user profile from the digital twin, and $C$ represents current network and device constraints.

A feedback loop is incorporated to continually refine the system’s accuracy and responsiveness. User feedback on video quality and streaming experience is collected and fed back into the digital twin to further personalize the experience and enhance system performance.

Experiments were conducted in a simulated streaming environment with over 500 participants using diverse devices under varied network conditions. The system dynamically adjusted video streaming parameters based on real-time updates from user digital twins.

We utilized: 1. A synthetic dataset created to simulate a range of user behaviors and network environments. 2. A real-world dataset from a commercial streaming service, comprising extensive logs of user interactions and network performance.

Benchmarks included established adaptive streaming algorithms such as MPEG-DASH and HLS.

We also conducted a user satisfaction survey, which revealed that 85% of participants reported higher satisfaction with the personalized streaming experience provided by our system, compared to 65% satisfaction with the traditional system.

These results demonstrate that the integration of user digital twins into video streaming systems significantly enhances video quality, reduces buffering events, and optimizes bandwidth usage, thereby improving the overall user experience. This underscores the potential of personalized adaptive streaming technologies to meet modern consumers’ demands for high-quality, reliable video streaming services.

Our findings confirm that dynamic adaptation to user-specific data, mediated by user digital twins, allows for a more personalized and responsive streaming experience. Key improvements observed include: - An increase in average video quality from 2.8 Mbps to 3.2 Mbps, while also reducing the variability in quality experienced by users. - A decrease in buffering events by nearly four times, underscoring the system’s ability to provide a smoother viewing experience. - A more judicious use of bandwidth, which not only conserves network resources but also ensures optimal video playback under varying network conditions.

The integration of user digital twins into streaming technologies represents a significant leap forward in the personalization of digital content delivery. This approach leverages real-time data to dynamically adjust both video quality and streaming parameters, thereby aligning more closely with the user’s current needs and context. Our work contributes to the literature by providing a robust framework for adaptive streaming that can be applied across various multimedia applications.

While our results are promising, the study has certain limitations that future research could address. The scalability of the system under extremely high user loads and diverse geographic distributions has not been fully tested. Additionally, the long-term impact of such personalized experiences on user behavior and satisfaction could be explored further.

Future research will also benefit from exploring more sophisticated machine learning models to enhance the accuracy and responsiveness of user digital twins. Another promising direction is the development of more granular user profiles that can adapt not only to individual preferences but also to subtle contextual changes.

In conclusion, the proposed methodology offers a tangible improvement in video streaming services by utilizing user digital twins. It not only enhances user satisfaction but also pushes the boundaries of what is technically feasible in adaptive video streaming. As streaming technologies continue to evolve, the principles laid out in this paper will likely find broader applications, potentially revolutionizing how multimedia content is consumed globally.