Hoeffding's inequalities for absolute values are derived by determining first the bound for the value, and then double it to arrive at a bound for the absolute value. But the question here asks for a bound related to the maximum of the absolute value, not to the absolute value of the maximum, so a direct examination of the absolute value is needed.
To compact notation, we define $S_k=\Big | X_1+\cdots+ X_k \Big |$. Then we are examining
$$\mathbb P\Big( \max_{1\le k\le n} \Big | \frac{S_k}{\sqrt{k}} \Big | \ge t\Big) = \mathbb P\Big( \bigcup_k \Big\{ \Big|\frac{S_k}{\sqrt{k}} \Big | \ge t \Big\} \Big)$$
Denote $I_Z$ the indicator function of the event $Z= \Big( \bigcup_k \Big\{ \Big|\frac{S_k}{\sqrt{k}} \Big |\ge t \Big\} \Big)$ and $I_1,..., I_n$ the indicator functions of the events, $\Big\{ \Big|\frac{S_1}{\sqrt{1}} \Big |\ge t \Big\},...\Big\{ \Big|\frac{S_n}{\sqrt{n}} \Big |\ge t \Big\} $, respectively.
Then by the properties of indicator functions
$$I_Z=\max\Big \{I_1,...,I_n\Big \}$$
Now if $I_Z =0$ it means that all $I_i,\; i=1,...,n$ are zero. If $I_Z=1$, it means that at least one of these individual indicator functions is unity, denote it $I_m$, and we have
$$I_m =1 \Rightarrow \Big|\frac{S_m}{\sqrt{m}} \Big |\ge t $$
Let $v = \text{argmax}_k \Big | \frac{S_k}{\sqrt{k}} \Big |$ and $\Big | \frac{S_v}{\sqrt{v}} \Big |$ the corresponding variable. Then in the case where $I_Z =1$ we have
$$\Big | \frac{S_v}{\sqrt{v}} \Big |\ge \Big|\frac{S_m}{\sqrt{m}} \Big |\ge t$$
Then in all cases, either when $I_Z=0$ or when $I_Z=1$ we have that, for some $h>0$,
$$I_Z \le \exp \left \{h\left (\Big | \frac{S_v}{\sqrt{v}} \Big |-t\right) \right \}$$
Therefore,
$$\mathbb P\Big( \max_{1\le k\le n} \Big | \frac{S_k}{\sqrt{k}} \Big | \ge t\Big) = \mathbb P\Big( \bigcup_k \Big\{ \Big|\frac{S_k}{\sqrt{k}} \Big | \ge t \Big\} \Big) = EI_Z \le E\exp \left \{h\left (\Big | \frac{S_v}{\sqrt{v}} \Big |-t\right) \right \}$$
$$\Rightarrow \mathbb P\Big( \max_{1\le k\le n} \Big | \frac{S_k}{\sqrt{k}} \Big | \ge t\Big) \le e^{-ht} E\exp \left \{h \Big | \frac{S_v}{\sqrt{v}} \Big | \right \} \qquad [1] $$
By Hoeffding's lemma, for a random variable $Y$, with $EY=0,\;a\le Y \le b$ we have, for any real $\lambda$
$$ E\left (e^{\lambda Y} \right) \le \exp \left(\frac{\lambda ^2 (b-a)^2}{8} \right) \qquad [2]$$
In our case, we have
$$\Big | \frac{S_v}{\sqrt{v}} \Big | = \frac{1}{\sqrt{v}}\Big |X_1 +...+ X_v\Big | \le \frac{1}{\sqrt{v}}\Big (\Big |X_1 \Big | +...+ \Big | X_v\Big | \Big) \le \frac{1}{\sqrt{v}}v = \sqrt{v}$$
the last inequality by the initial assumptions. Since $v\le n$ we have
$$0\le \Big | \frac{S_v}{\sqrt{v}} \Big | \le \sqrt{n} \Rightarrow -E\Big(\Big | \frac{S_v}{\sqrt{v}} \Big |\Big) \le \Big | \frac{S_v}{\sqrt{v}} \Big | - E\Big(\Big | \frac{S_v}{\sqrt{v}} \Big |\Big) \le \sqrt{n} -E\Big(\Big | \frac{S_v}{\sqrt{v}} \Big |\Big) $$.
We now have a variable with zero expected value and bounded. The length of the interval is $b-a = \sqrt{n} -E\Big(\Big | \frac{S_v}{\sqrt{v}} \Big |\Big) + E\Big(\Big | \frac{S_v}{\sqrt{v}} \Big |\Big) = \sqrt{n} $ and $\lambda = h $.
Inserting these values into Hoeffding's lemma and simplifying we get
$$ E\exp \Big \{h\Big | \frac{S_v}{\sqrt{v}} \Big | - hE\Big(\Big | \frac{S_v}{\sqrt{v}} \Big |\Big)\Big \} \le \exp \left(\frac{nh^2}{8} \right) $$
$$\Rightarrow E\exp \Big \{h\Big | \frac{S_v}{\sqrt{v}} \Big | \Big \} \le \exp \left(\frac{nh^2}{8} \right)\exp\Big \{hE\Big(\Big | \frac{S_v}{\sqrt{v}} \Big |\Big)\Big \} \qquad [3]$$
Note that the exponential that moved to the right hand side does not contain any random quantities that's why the expected value has disappeared. From previously we have
$$ \Big | \frac{S_v}{\sqrt{v}} \Big | \le \sqrt{n} \Rightarrow E\Big | \frac{S_v}{\sqrt{v}} \Big | \le \sqrt{n} \Rightarrow \exp\Big \{hE\Big(\Big | \frac{S_v}{\sqrt{v}} \Big |\Big)\Big \} \le \exp\Big \{h\sqrt{n}\Big \} \le \exp\Big \{h^2n\Big \} \; [4] $$
Inserting the RHS of $[4]$ into $[3]$ and back in $[1]$ we obtain
$$ \mathbb P\Big( \max_{1\le k\le n} \Big | \frac{S_k}{\sqrt{k}} \Big |\Big )= \mathbb P\Big( \Big | \frac{S_v}{\sqrt{v}} \Big | \ge t\Big) \le \exp \left(-ht +\frac{9nh^2}{8} \right) \qquad [5]$$
Minimizing the RHS over $h$ we obtain $h^* = \frac {4}{9n}t$ and inserting into $[5]$ we finally obtain
$$ \mathbb P\Big( \max_{1\le k\le n} \Big | \frac{S_k}{\sqrt{k}} \Big |\Big )=\mathbb P\Big( \Big | \frac{S_v}{\sqrt{v}} \Big | \ge t\Big) \le \exp\Big \{-\frac{2}{9n}t^2\Big \} \qquad [6] $$
...which is a bound related to the number of r.v.'s involved.
Best Answer
Your second suggestion works. Let $r$ be the greatest natural number satisfying $2^r \leq n \log n$. Then $X_1 > n \log n$ implies $X_1 \geq 2^{r+1}$. So, $$P(X_1 > n \log n) \leq P(X_1 \geq 2^{r+1}) = \sum_{k=r+1}^\infty \frac{1}{2^k}= \frac{1}{2^r} <\frac{2}{n\log n},$$ where the last inequality is just rearranging $2^{r+1} > n \log n$.