# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 2 Related Work  
In recent times, during the execution of interactive services in real-time, QoS requirements have grown and created congestion. Delay and jitter are regarded to be network performance indicators. Video quality correlates to network performance as well as the state of the received packets. Thus, just offering high network performance does not really ensure video quality. As cited in [9], a considerable impact is caused by the delay jitter on QoS versus packet loss and delay. The traditional scheduler algorithms do not account for the impact cast by network jitter on transmitting packets when it comes to real-time applications, such as online video conferencing or online gaming. The quality of viewing is significantly impacted by jitter. For such services, controlling beam jitter is regarded to be an indispensable part of maintaining QoS [10]. As per a recent paper [11] that examined the impact caused by jitter on video streaming in LTE networks, well-known downlink scheduling (PF, MLVDF, EXPRule) was clearly affected by jitter. QoS (QoS factors) can be defined as a group of different parameters that can impact the quality of video transmission. These parameters can easily impact the video quality of broadcasting and they also serve as measurement indicators for service providers. Indeed, even though the LTE radio network is regarded as decreasing latency compared to previous cellular technologies, it does not employ a fixed delay, as faulty transmissions are fixed via rapid re-transmissions [12]. In this case, transmissions occurring in the LTE network can result in jitter that can deteriorate the quality of applications in real-time. Thus, considering jitter as an aim and not as a limitation is key for certain applications, as mentioned in [13; 14]. While delay jitter pertaining to data packets can impact the quality of experience for users, as stated in [15], it is also regarded as an essential QoS metric for real-time applications. The difference pertaining to packet delay in a specific stream is measured via jitter, which is key for real-time interactive services such as video streaming and VoIP [16]. In an ideal scenario, even though the packet delivery is set in a totally periodic manner and an equal reciprocal flow is generated by the source, the network produces an inevitable jitter because of the change in queue and propagation delay, and the arrival of the packets at the destination in different time range [17]. Thus, various researchers have focused on analysis and control of jitter in LTE networks in order to enhance performance. [PERSON], and [PERSON] [7] analysed behaviour pertaining to the jitter and delay in the LTE network by putting forward a jitter model that considered the service time for the total arrival rate for each data flow. The Poisson process was regarded as a traffic model and the results demonstrated that when the traffic load jitter and delay were increased, they behaved differently, wherein delay increased and the jitter reduced. [PERSON]. [17] put forward a method to improve radio communications performance via radio level feedback. A jitter buffer was implemented to keep the levels of both delay-of-packet and PLR at a minimum. This method involved calculating a projected delay related to the radio events, receiving incoming transmission packets through a wireless communication medium, recognising radio events that would impact the timing of transmission packets to be received in the future, and holding and queuing the incoming transmission packets, as well as determining an effective delay as per the projected delay, wherein for controlling, an effective delay is employed on release of the queued incoming transmission packets. For heterogeneous downlink traffic LTE networks, [18] put forward a QoS-aware energy and jitter efficient model.