Quality-aware massive content delivery in digital twin-enabled edge networks

Massive content delivery will become one of the most prominent tasks of future B5G/6G communication. However, various multimedia applications possess huge differences in terms of object oriented (i.e., machine or user) and corresponding quality evaluation metric, which will significantly impact the design of encoding or decoding within content delivery strategy. To get over this dilemma, we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision (QoD) or Quality-of-Experience (QoE) for the guidance of content delivery. Then, in terms of machine-centric communication, a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment, which can achieve a balance among decision-making, delivered content, and encoding latency. Finally, in terms of user-centric communication, by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams, we develop a QoE-driven video enhancement scheme to supply high data fidelity. Numerical results demonstrate the remarkable performance improvement of massive content delivery.