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Abstract-In this paper, we formulate the collaborative multi-user wireless video transmission problem as a multiuser Markov decision process (MUMDP) by explicitly modeling the users' heterogeneous video traffic characteristics and time-varying network conditions. Our formulation further considers the existing coupling between the wireless users when determining the optimal cross-layer transmission strategies that should be adopted by the users to maximize their long-term system utility (i.e. video quality). These environment dynamics are often ignored in existing multi-user video transmission solutions. To comply with the decentralized architecture of multiuser communication scenarios, we propose to decompose the MUMDP into local MDPs, which can be autonomously solved by individual users, using Lagrangian relaxation. Unlike in conventional multi-user video transmission solutions stemming from the network utility maximization framework, the proposed decomposition enables each wireless user to individually solve its own dynamic cross-layer optimization and the network coordinator to update the Lagrangian multipliers (i.e. resource prices) based on not only current, but also future resource needs of all users, such that the long-term video quality of all users is maximized. However, solving the MUMDP requires statistical knowledge of the experienced environment dynamics, which is often unavailable before transmission time. To overcome this obstacle, we propose a novel online learning algorithm, which allows the wireless users to update their policies in multiple states during one time slot. This is different from conventional learning solutions, which update one state per time slot. The proposed learning algorithm can significantly improve the learning performance, thereby dramatically improving the video quality experienced by the wireless users over time. Our simulation results demonstrate the efficiency of the proposed MUMDP framework as compared to conventional multi-user video transmission solutions.

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Amphiphobic, microporous polyurethane (PU) composite microfibrous membranes exhibiting robust waterproof and breathable performances were prepared by the introduction of a novel synthesized fluorinated PU (FPU) containing head perfluoroalkane segment. By employing the FPU incorporation, the pristine PU membranes were endowed with the superhydrophobicity with water contact angle of 156u and the oleophobicity with oil contact angle of 145u. The role of FPU for the tuning of the morphology, surface wettability and mechanical property of resultant membranes were discussed, and a plausible twostep break mechanism upon the external stress is proposed. The quantitative fractal dimension analysis using N 2 adsorption method has confirmed the correlation between the hierarchical roughness and amphiphobicity. Furthermore, the as-prepared membranes exhibited high water resistance (39.3 kPa), good air permeability (8.46 L m 22 s 21 ) and water vapor transmittance (0.384 kg m 22 h 21 ), and comparable tensile strength (10 MPa), suggesting their use as promising materials for a variety of potential applications in protective clothing, bioseparation, membrane distillation, tissue engineering and catalyst carriers, etc., and also provided new insight into the design and development of functional microfibrous membranes through FPU incorporation.