Fig 2 - uploaded by Chun Pong Lau

Content may be subject to copyright.

## Contexts in source publication

**Context 1**

... the uniform case, all of the TV channels has 200 audiences. Figure 2 shows the function f n (q), which is the total number of audiences when broadcasting q popular TV channels. In Zipf distribution, f n (q) has a low starting number of audiences and grows quickly when q increases. ...

**Context 2**

... numerically evaluate the lemmas by applying different cases and distributions for n(c), which are different distri- butions for the size of audience of TV channels. Reported in [13] and [14], the popularity of TV channels following Zipf-like distribution. We use Zipf distribution with parameter s = 1 in our evaluation. In addition, we include another two distributions, Zipf-Mandelbrow and Zeta, with parameter s = 2 and s = 3, for comparison. Furthermore, two extreme cases are added in the evaluation. One case is all of the TV channels having the same size of audience, which is an uniform distribution. It is related to Case 1 in Lemma 1. Another case is when all of the audiences watching only the most popular TV channel, which is denoted as n(2) = 0 in the figures and related to Case 2 in Lemma 1. For the other settings, we assume there are 2000 audiences in the cell and 10 channels are being selected to be broadcasted. The required bitrate for each TV channel is 1 Mb/s. Figure 1 shows the number of audiences for each channel. The channels in the x-axis are sorted in a descending order Zipf, s=1 Zipf−M., s=2 Zeta, s=3 n(2)=0 uniform Zipf, s=1 Zipf−M., s=2 Zeta, s=3 n(2)=0 uniform according to the popularity, which is the size of audience. All of the cases follows Definition 1 that the less popular channels have fewer or equal number of audiences. In Zipf distribution, the most popular TV channel has 683 audiences. The numbers of audiences drop gently to 68 audiences in the least popular channel. In Zip-Mandelbrow distribution, it starts with 1291 audiences in the most popular channel, drop quickly and negative exponentially to only 13 audiences in the least popular channel. In Zeta distribution, it starts with 1664 audiences and has a fastest drop among these three distribution to having less than 10 audiences in the last five TV channels each. In the special case that n(2) = 0, all of the 2000 audiences watch the most popular TV channel and no audiences for the remains. For the uniform case, all of the TV channels has 200 audiences. Figure 2 shows the function f n (q), which is the total number of audiences when broadcasting q popular TV channels. In Zipf distribution, f n (q) has a low starting number of audiences and grows quickly when q increases. In Zipf-Mandelbrow and Zeta distribution, both of them start with a higher number of audiences and grow slower than the Zipf distribution. In n(2) = 0, the first TV channel has all of the 2000 audiences therefore, it remains constant and not growing in f n (q). In Zipf, s=1 Zipf−M., s=2 Zeta, s=3 n(2)=0 uniform Fig. 4. Broadcast Efficiency the uniform distribution, it grows linearly and has the fastest growth rate compare to the other cases. This validates Lemma 1 that f n (q) is an increasing function and the fastest growth rate of the function is linear. Figure 3 shows the function f r (q), which is the total require bitrate for broadcasting q TV channels. From the assumption in (1), all of the TV channels have the same bitrate. Therefore, f r (q) grows exactly linearly. This validates Lemma 2 that f r (q) is a strictly linear growth function. Figure 4 shows the broadcast efficiency for each case. It shows that E(q) is a decreasing function. The case n(2) = 0 has the fastest decreasing rate since broadcasting more channels in this case does not serve any more audience. It is a waste of resources if broadcasting more than one TV channel when there is no audience in the remaining 9 TV channels. Zipf distribution has a slower decreasing rate than the other two distributions because more audiences in the unpopular channels. It leads to a conclusion that a more even distribution of audiences on the TV channels helps to slow down the decreasing rate of broadcast efficiency when broadcasting more TV channels. In the uniform distribution, broadcast efficiency remains constant since both the numerator and denominator of E(q) are linear. It is also the lower In summary, the numerical evaluation validates the lemmas presented in the previous section. We further show the effect of broadcasting different numbers of TV channels on broadcast efficiency in the following subsection by a real-life ...

## Similar publications

## Citations

... In fact, the more users in a cell at a given time, the more efficient it becomes to deliver popular contents in a single broadcast wireless transmission [7]. Therefore, caching in the BS is insufficient for improving spectral efficiency in the RAN. ...

Today's modern communication technologies such as cloud radio access and software defined networks are key candidate technologies for enabling 5G networks as they incorporate intelligence for data-driven networks. Traditional content caching in the last mile access point has shown a reduction in the core network traffic. However, the radio access network still does not fully leverage such solution. Transmitting duplicate copies of contents to mobile users consumes valuable radio spectrum resources and unnecessary base station energy. To overcome these challenges, we propose huMan mObility-based cOntent Distribution (MOOD) system. MOOD exploits urban scale users' mobility to allocate radio resources spatially and temporally for content delivery. Our approach uses the broadcast nature of wireless communication to reduce the number of duplicated transmissions of contents in the radio access network for conserving radio resources and energy. Furthermore, a human activity model is presented and statistically analyzed for simulating people daily routines. The proposed approach is evaluated via simulations and compared with a generic broadcast strategy in an actual existing deployment of base stations as well as a smaller cells environment, which is a trending deployment strategy in future 5G networks. MOOD achieves 15.2% and 25.4% of performance improvement in the actual and small-cell deployment, respectively.

... In the future fifth- generation (5G) networks, content providers would be able to deploy their distribution algorithms through the functionalities of software defined network (SDN) and network function visualization (NFV) onto the core network (CN) and the radio access network (RAN) [9]. In the SDN approach, a cloud-based software defined controller (SDC) receives high-level services policies from content providers and implements control signal in the CN and RAN for radio resources allocation, content distribution schedule, and cooperated broadcasting and multicasting, such as researches in [10]- [12]. ...

Traditional live television (TV) broadcasting systems are proven to be spectrum inefficient. Therefore, researchers propose to provide TV services on fourth-generation (4G) long-term evolution (LTE) networks. However, static broadcast, a typical broadcasting method over cellular network, is inefficient in terms of radio resource usage. To solve this problem, the audience-driven live TV scheduling (ADTVS) framework is proposed, to maximize radio resource usage when providing TV broadcasting services over LTE networks. ADTVS, a system-level scheduling framework, considers both available radio resources and audience preferences, in order to dynamically schedule TV channels for broadcasting at various time and locations. By conducting a simulation using real-life data and scenarios, it is shown that ADTVS significantly outperforms the static broadcast method. Numerical results indicate that, on average, ADTVS enables substantial improvement to broadcast efficiency and conserves considerable amount of radio resources, while forgoing less than 5% of user services compared to the benchmark system.