Digital technologies like DLNA (Digital Living Network Alliance) proliferating the home networking segment, media-related services are paving way for significant enhancements to user's entertainment experience. A standard software platform like Android brings in a plethora of opportunities to offer rich multimedia content on devices. While the fundamental aspect of these new experiences being the ability to enjoy high definition multimedia content in any location throughout the home, our investigation shows the content is delivered on Best-Effort basis, especially on interoperable devices. Motivated by these observations, we propose an adaptive QoS framework on Android to address home-media sharing services on various inter-operable DLNA-enabled audio/video terminals. In this paper, we propose a novel adaptive QoS framework on Android to enhance the quality of experience of applications in wireless home networks. The outline of paper is as follows: We first discuss a few related works which investigate the reasons for performance bottleneck in media delivery in inter-networked environment. Consequently, we present a different perspective of identifying, classifying, prioritizing, assigning and associating primary Network QoS parameters to various device classes specified by DLNA thereby guaranteeing to approximate the optimal solution within a constant factor. In particular, we point out how the chain of delegation process employed in the framework can improve QoE reasonably well in DLNA Enabled Home Environment. Our approach in this work was on an optimized QoS client architecture that maximizes the average quality of multimedia content delivered on software platforms like Android. We also show the need for feedback mechanism in such a setting to determine optimal values for primary QoS parameters in a progressive manner. To validate our approach, we have also conducted a measurement on a prototype developed using our architecture. We observed significant improvem- - ent for the entire application when using our framework leading us to conclude that the system is effective at improving performance significantly. We also summarize the performance characteristics for the trial experiments conducted.