In recent years， large language models （LLMs） have achieved remarkable progress in natural language processing （NLP）， demonstrating exceptional capabilities in language understanding and generation. These advancements have driven widespread applications in tasks such as text generation， machine translation， question answering， text summarization， and text classification. However， despite their impressive performance in handling and generating text， LLMs face notable limitations when handling highly complex multimodal tasks， particularly in the domain of video question answering （Video QA）. Video QA is a particularly challenging task that requires models to comprehend and generate responses based on dynamic visual content， which often includes temporal and auditory information. Unlike static images or purely textual contents， video data contains inherent temporal dependencies， where the meaning of events and actions unfolds over time. This temporal dimension adds substantial complexity to the understanding process because models must not only interpret individual frames but also maintain coherent understanding across sequences of frames within the broader video context. Consequently， effective Video QA demands advanced temporal information processing capabilities that many LLMs， primarily designed for static text， often struggle to handle adequately. Moreover， the multimodal nature of video， which often involves the integration of visual， auditory， and occasionally textual cues， further complicates the task. Effective Video QA requires the model to seamlessly fuse information across these different modalities， ensuring accurate interpretation and response to questions regarding video content. This process involves understanding visual scenes， recognizing speech or background sounds， and correlating them with the corresponding textual information. The challenge lies not only in processing each modality independently but also in establishing meaningful connections between them to generate coherent and contextually appropriate responses. This paper presents a comprehensive review of the current state of research on Video QA models based on large language models. The technical characteristics， strengths， and weaknesses of non-real-time and real-time Video QA models are also investigated. Non-real-time Video QA models typically operate on pre-recorded video content， allowing them to access and analyze the entire video sequence before generating responses. These models can leverage global contextual information， making such models particularly effective for tasks that require video content analysis， such as video summarization or detailed scene interpretation. However， they may struggle with efficiency and scalability， particularly when handling long videos or large datasets. In contrast， real-time Video QA models are designed to process video streams as they are received， increasing their suitability for applications requiring immediate responses， such as live video monitoring or interactive video systems. However， these models must maintain a balance between processing speed and accuracy due to their frequently limited access to the full temporal context of the video. The paper discusses the challenges encountered by these models in maintaining performance under real-time constraints， including efficient computation and prediction capability based on partial information. Additionally， the paper explores the commonly used datasets in Video QA research， highlighting their features， limitations， and the types of tasks they are designed to address. The evaluation of Video QA models is also examined， focusing on the metrics and benchmarks used to assess their performance. Understanding the strengths and weaknesses of different datasets is crucial for advancing the field， helping in the identification of gaps in current research and guiding the development of robust and versatile models. Finally， the paper addresses the extensive challenges and bottlenecks in the field of Video QA， including the difficulties in scaling models to handle large and diverse video datasets， the need for efficient multimodal fusion techniques， and the computational demands associated with video data processing in real-time. The discussion is further extended to consider the potential future research directions in Video QA， with particular emphasis on improving the temporal reasoning capabilities of LLMs， enhancing their multimodal integration， and developing efficient model architectures that can operate effectively under resource constraints. Overall， while large language models have presented new possibilities in the field of video interpretation， considerable challenges remain in adapting these models to the specific demands of Video QA. Through the systematic review of the current advancements and the presentation of the key obstacles and future directions， this paper aims to contribute to the ongoing efforts to develop highly capable and intelligent multimodal AI systems. The field must continue innovations in the following areas： temporal modeling， where novel architectures that can effectively capture long-range dependencies in video sequences are needed； multimodal representation learning， where sophisticated approaches for integrating visual， auditory， and textual features could yield substantial improvements. Furthermore， the development of highly efficient training paradigms that can address the computational intensity of video processing while retaining model performance is essential for practical applications. Another critical area for future work focuses on the creation of highly comprehensive and challenging benchmark datasets that effectively reflect real-world scenarios， pushing the boundaries of what current models can achieve. As research in this area progresses， addressing these challenges will be crucial for realizing the full potential of LLMs in video interpretation applications. Achieving this goal will require AI systems that can interpret and reason about dynamic visual content with a level of proficiency comparable to human cognition. The integration of advanced techniques from computer vision， speech processing， and natural language understanding will be pivotal in developing truly multimodal systems capable of managing the complexity and variability in real-world video data. Through continued innovation and interdisciplinary collaboration， the field can overcome current limitations and drive the development of next-generation video understanding technologies with broad applicability across domains such as education， entertainment， surveillance， and human-computer interaction.