The widespread adoption of video streaming applications has experienced a continual upsurge, necessitating advancements in underlying technologies to ensure a seamless user experience. Central to the realm of video streaming is video coding, a key element in the efficient transmission of multimedia content.In the context of HTTP Adaptive Streaming (HAS), videos are encoded at multiple bitrate-resolution pairs, collectively forming what is known as the bitrate ladder. This approach allows users to adapt to varying network conditions and select the most appropriate bitrate-resolution pair for a given content, thereby enhancing the overall Quality of Experience (QoE). This research extends its focus beyond (i) enhancing individual bitrate-resolution pairs to also include (ii) optimizing the construction of the entire bitrate ladder. For the former, innovative solutions utilizing content-aware deep neural networks are proposed. The primary objective is to elevate the quality of single bitrate-resolution pairs through the application of advanced deep learning techniques tailored to the unique characteristics of the content.In pursuit of the latter objective, solutions are sought to determine the number of bitrate-resolution pairs in the bitrate ladder and their corresponding encoding parameters, such as optimal bitrate and resolution. (i) Network-assisted or (ii) content-aware approaches may be employed to find the optimal number of bitrate-resolution pairs, while content-aware solutions can assist in determining optimal resolutions, frame rates, and other parameters for the selected bitrates. Attention is also given to online bitrate ladder constructions, addressing the latency challenges inherent in such scenarios.Recognizing that video coding is incomplete without proper assessment of quality, efforts are directed towards integrating video quality and coding. Real-time quality metrics are identified, and key parameters influencing Quality of Experience (QoE) are considered. Additionally, solutions are explored in video coding and transcoding to reduce encoding time/costs and energy consumption while minimizing quality degradation. This holistic approach aims to contribute comprehensively to the enhancement of video streaming technologies.