Video content is an increasingly prevalent contributor of Internet traffic. The proliferation of available video content has been fuelled by both Internet expansion and the growing power and affordability of viewing devices. Such content can be consumed anywhere and anytime, using a variety of technologies. The high data rates required for streaming video content and the large volume of requests for such content degrade network performance when devices compete for finite network bandwidth. The results are prolonged startup delays and frequent stops for rebuffering during video playout. Such effects are especially significant for third level educational settings where, on-demand access to high quality educational video content by on-campus students is an increasingly important requirement. Although purely online courses are attracting growing interest traditional campus-based classes remain large. In the latter setting, frequently large numbers of students may simultaneously request identical video content. Adaptive HTTP-based streaming technologies such as DASH introduce client-controlled delivery of video in order to dynamically adapt to varying bandwidth and viewing device characteristics. However, although DASH allows for individual clients to adapt to network conditions it does not support multiple local clients in co-ordinating their actions. Thus, despite DASH-aware devices, problems remain when numerous local clients simultaneously request high bandwidth video. This thesis addresses the problem of quality degradation in personalised video delivery by developing mechanisms which raise video quality levels in a campus setting. A DASH-based Performance Enhancement Architecture (DPEA) is proposed to enhance the performance of existing personalised systems. Under DPEA, the quality of the delivered video is increased by deploying a Performance Oriented Adaptation Agent (POAA) that considers the characteristics of the links connecting video providers and the campus network in order to select remote servers with the best current performance. Furthermore, this solution proposes a DASH-based Adaptive Video Distribution Solution (DAV) which considers both device characteristics and recently downloaded (locally available) video segments in order to improve the content delivery process thereby improving the video viewing experience. The proposed solutions maintain satisfactory quality levels when multiple requests for identical video content are generated in an on-campus setting. The solutions are evaluated by simulations in which various network parameters are considered. The results clearly demonstrate improved video quality when the proposed solutions are deployed.