Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamic video streaming protocols allow video clients to adaptively choose video rates to improve vier Quality-of- Experience (QoE). Existing works on rate adaptation choose the video rate according to network capacity or buffer state, hover, they didn't consider the early quitting problem. Most viers quit before the video ends, in which case optimal solutions for complete video sessions may become suboptimal. ,  design a video rate adaptation scheme which considers incomplete video sessions and aims at maximizing the quality of the video chunks that are actually watched by viers.  formulate the video rate adaptation problem as a Markov Decision Process (MDP), in which the viers' decision of quitting depends on the video quality and the rebuffer time. To solve the MDP problem,  propose two online learning algorithms for regular viers and new viers with no viewing history, respectively. The simulation results show that the proposed scheme increases the video quality {truly} consumed by the viers, thus improving the vier QoE.
['Yanjiao Chen', 'Fan Zhang', 'Kaishun Wu', 'Qian Zhang']
I am writing a research paper on a idea QoE-Aware Dynamic Video Rate Adaptation
[{"from": "human", "value": "I am writing a research paper on a idea QoE-Aware Dynamic Video Rate Adaptation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamic video streaming protocols allow video clients to adaptively choose video rates to improve vier Quality-of- Experience (QoE). Existing works on rate adaptation choose the video rate according to network capacity or buffer state, hover, they didn't consider the early quitting problem. Most viers quit before the video ends, in which case optimal solutions for complete video sessions may become suboptimal. ,  design a video rate adaptation scheme which considers incomplete video sessions and aims at maximizing the quality of the video chunks that are actually watched by viers.  formulate the video rate adaptation problem as a Markov Decision Process (MDP), in which the viers' decision of quitting depends on the video quality and the rebuffer time. To solve the MDP problem,  propose two online learning algorithms for regular viers and new viers with no viewing history, respectively. The simulation results show that the proposed scheme increases the video quality {truly} consumed by the viers, thus improving the vier QoE."}]